No, homeopathic remedies can’t “detox” you from exposure to Roundup: Examining Séralini’s latest rat study

Image by Mark Philpott, shared via the Creative Commons license.

Image by Mark Philpott, shared via the Creative Commons license.

One of my main goals for this blog is to help people learn how to evaluate scientific studies. To that end, I have written several posts that dissect papers and explain either why they are robust or why they are untrustworthy (for example, see my posts on Splenda, GMOs, and vaccines). These posts have the dual goals of debunking bad science and helping people think critically, and the time has come for me to write another one of these posts. Earlier this week, someone showed me a recent study which they claimed proved that detoxing is a real thing and there are natural remedies that help your body rid itself of toxins. The study in question is, “Dig1 protects against locomotor and biochemical dysfunctions provoked by Roundup.” As you might imagine, it is less than an exceptional paper. Indeed, it was such a blatantly horrible paper that I thought it would make a good teaching tool to illustrate some of the things that you should watch out for in scientific studies. I’ll summarize the main points below, but I encourage you to read the paper for yourself and see if you can spot the problems with it before you read any further.

I have organized this post in a progression starting with problems that are concerning, but not fatal, then moving to issues that limit the papers conclusions, and ending with problems that completely nullify the paper. I have chosen this order because it is also the progression of knowledge required to spot the problems. Most people should be able to see the red flags that I will start with, so even if you don’t have the statistical knowledge to spot the more technical problems, you can still use those early warning signs as clues that the paper should be scrutinized closely before accepting it.

Authors and conflicts of interest

It is always a good idea to look at both the authors who wrote the paper and the funding sources. Some scientists have reputations for publishing crappy or even fraudulent research, and you should be wary of them. Similarly, financial conflicts of interest should make you more skeptical of a study. Having said that, I want to be absolutely, 100% clear that you cannot claim that a study is wrong simply because of the people who wrote it or their funding sources. Those things are red flags that should make you cautious and should make you look at a paper more closely, but they are not in and of themselves enough to sink a paper (i.e., using them as the basis for outright rejection is an ad hominem/genetic fallacy). Let me put it this way, if I have a study that has some sections that are unclear, but it was written by reputable scientists and did not have any conflicts of interest, then I will probably give the authors the benefit of the doubt. In contrast, if that same paper had been written by notoriously second-rate scientists and/or had serious conflicts of interest, I would be far less willing to give the authors a pass. Another consideration is the general body of literature surrounding the paper. Extraordinary claims require extraordinary evidence, and it is always suspicious when a paper that conflicts with a large body of literature was also written by a fringe scientist and funded by people who stand to benefit from the paper.

Now that all of that has been said, let’s look at the paper itself. The first thing that jumps out is the fact that the final author on this paper is Gilles-Éric Séralini (the last author position is usually reserved for the most senior scientist who was in charge of the overall project). Séralini, for anyone who doesn’t know, is infamous for publishing low-quality, fringe studies in opposition to biotechnology (specifically GMOs). Indeed, he was the author on the infamous rat study which purported to show that GMOs caused cancer in rats, but actually only showed that Séralini doesn’t understand the concept of a proper control. Indeed, the study was so horrible that it was retracted, at which point, Séralini re-published it through a minor and questionable journal that didn’t even bother to send the paper out for review (hardly the actions of a proper scientist).

We aren’t off to a good start, but things get even worse when we look at the funding. The paper is about the supposed benefits of a homeopathic product known as Digeodren (aka Dig1), but it was funded by the company that produces Digeodren (Sevene Pharma). The authors try to get around this by saying, “The authors declare that they have no competing interests. The development of Dig1 by Sevene Pharma was performed completely independently of its assessment,” but that is just a red herring. The fact that the development and testing of Digeodren were separate is completely irrelevant. The point is that the study was funded by the same company that both produces Digeodren and stands to benefit from it. That is, by any reasonable definition, a serious conflict of interest.

Again, to be 100% clear, I am not saying that the study is invalid because it was funded by Sevene Pharma, nor am I saying that it is invalid because it was conducted by Séralini, but, both of those things are serious red flags, and the rest of the study will need to be impeccable if we are going to overlook them.

The journal that published the paper

Another quick and easy thing to look at is the quality of the journal that published the paper. You need to be careful when using this tool, however, because there is plenty of good research that is published in minor journals simply because it is not of a “broad enough scope” or “wide enough impact” to interest major journals. So you need to judge journal quality against the claims being made in a paper. In other words, when a paper is making extraordinary claims but was published in a minor journal, you should be skeptical. As with the authors and conflicts of interest, however, this is not enough to sink a paper, but it is a red flag to watch out for.

So how does our paper do? Well, it is claiming not only that a homeopathic remedy works (more on that in a minute), but also that it can help to remove toxins from your body. Both of those are extraordinary claims that fly in the face of a large body of literature. In other words, if those claims were well supported, then this paper would be of extremely wide interest and should be published in a top journal. Therefore, the fact that it showed up in a fringe journal (BMC Complementary and Alternative Medicine) is yet another warning sign that something is seriously wrong with it.

Extraordinary claims require extraordinary evidence

As alluded to earlier, you should always consider the a prior plausibility of the claims being made in a paper (i.e., how likely are they to be true given the results of other studies). In other words, if a paper is simply reporting something that dozens of other papers have reported, then you don’t need to be too critical (you should still evaluate it, but it requires less scrutiny). In contrast, when a paper is reporting something extraordinary that conflicts with numerous other papers, then the paper needs to present extraordinary evidence to support its claims.

In this case, the claims of the paper are in fact quite extraordinary. First, it is testing a homeopathic remedy. I explained the folly of homeopathy in more detail here, but in short, it relies on the utterly absurd notions that diluting something makes it stronger, like cures like, and water has memory. In other words, homeopathy violates several of our most fundamental scientific concepts. Again, that does not automatically mean that it is wrong because it is always technically possible (albeit very unlikely) that those concepts are in fact flawed. However, if you want to claim that they are flawed, you need to provide some extraordinary evidence, and in the case of homeopathy, that evidence is nowhere to be found. Indeed, systematic reviews of the literature show that homeopathy is nothing more than a placebo. Similarly, detox supplements, shakes, foot baths, etc. are scams. Your body already does a very good job of keeping potentially harmful chemicals at safe levels, and no natural remedies have been shown to actually remove toxins.

Given the weight of evidence against the claims being made by this paper, it would need to be an outstanding study to be convincing. It would need enormous sample sizes, extremely rigorous controls, careful statistics, etc. In other words, it would need to meet an extremely high bar, but as I will demonstrate, it fails to do that.

The importance of the introduction

You can often tell a lot about a paper by its introduction (called the “Background” in this journal). This is where authors are supposed to review the current state of knowledge on the topic of the paper and make the case for why their study is interesting and important. When authors fail to do that convincingly, it is often a sign of underlying problems with the study.

In this case, the introduction is quite short and has several irregularities. First, multiple of the papers that were cited were other Séralini studies. That is not a good sign. There is a lot of other relevant literature out there that should have been included (much of which disagrees with Séralini’s studies). Similarly, several of the cited studies are questionable at best. Indeed, one of their central arguments hangs on a citation to the aforementioned GMO rat study that was so flawed that it was retracted.

Further, the authors cited several relevant papers about the properties of the active ingredients in Digeodren, but they totally failed to mention that Digeodren is a homeopathic remedy and those active ingredients are only present at extremely low concentrations (in this case about 1–10 parts in 100,000). They did mention this briefly in the methods, but its omission from the introduction is extremely troubling. If you are going to do a study on homeopathy, you had dang well better discuss the existing literature on that topic.

Methods: animal trials

Now we finally get to the core of the paper itself, and the first thing that jumps out is the fact that this was an animal trial. As I explained in more detail here, humans have a different physiology than other animals. As such, animal studies have a fairly limited applicability to humans. Therefore, they should be used to identify treatments that are good candidates for human trials, but you cannot jump from saying that something works in rats to saying that it works in humans. To be clear, I am not saying that the results of animal studies are wrong. Indeed, in many cases, the drug in question does in fact work in the species that was being tested, but the fact that it worked in that species does not automatically mean that it will work in humans. As a result, you need to be careful about applying the results of animal studies to humans.

Methods: experimental design

Their experimental design was pretty simple. They took a group of 160 rats and randomly divided them into four groups of 40. One group was kept as a control and did not receive any form of treatment, one group received Roundup in its water, one group received Digeodren in its water, and one group received both Digeodren and Roundup in its water. That’s not a terrible design, but it is also not a great design. A much better approach would have been to include a blocking element.

Imagine, for example, there was a slight thermal gradient in the lab where the rats were housed, and the cage rack containing the control mice ended up being on the warm end, while the cage rack with the Roundup mice ended up being on the cool end. That introduces a new variable and can have dramatic effects on the study. You’d be surprised how much a little thing like that can skew a result. Thus, a much better approach is to do what is known as “blocking.” To do this, instead of having four sets of cages, with each set containing a different group, you have members of each treatment group in each set of cages. In other words, for each set of cages, you randomly select 10 cages from each of your four treatment groups, that way, each set (what we would call a “block”) has 10 individuals from each treatment group (the position of the cages within each block should also be randomized). Now, if there is a thermal gradient (or any other confounding factor), it balances out because it affects all of your treatment groups equally. Further, you can (and should) including that blocking variable in your analyses to actually test for confounding factors across your sets of cages. Failing to block the experiment like that is not always fatal to an experiment (depending on the type of experiment), but it does make me far less confident in the results, and remember, to accept this particular paper, it needs to be an extraordinarily good paper.

This shows three different setups for the same experiment comparing four groups of 40 rats (I am assuming one rat per cage). On the far left, you have what seems to be being described by this study (each experimental group is separate). This is a weak design. A better design is what you see in the middle where you have representatives from each experimental group within each "block." The best design is then to randomize the location of the cages within those blocks (as seen on the far right).

This shows three different setups for the same experiment comparing four groups of 40 rats (I am assuming one rat per cage). On the far left, you have what seems to be being described by this study (each experimental group is separate). This is a weak design. A better design is what you see in the middle where you have representatives from each experimental group within each “block.” The best design is then to randomize the location of the cages within those blocks (as seen on the far right).

A second issue is that this experiment wasn’t blinded. In other words, the researchers knew which rats were in each treatment group. That makes it very easy for their biases to inadvertently influence the experiment, especially given that one of the researchers has a reputation for publishing agenda-driven papers (again, even a slight difference in how the rats were treated could have affected things).

Note: the authors were a bit vague about how their cages were set up, so it is not clear how many rats were in each cage or how many sets of cages there were. However, it is clear that they did not use a proper blocking design.

Methods: The doses

Anytime that you are looking at a toxicology study, you have to look at the doses to see if they are reasonable. Remember, everything (even water) is toxic at a high enough dose. So when a study is looking at an environmental chemical like Roundup, it is important that they use a dose that you would realistically be exposed to in the environment. Otherwise, the study has no real applicability.

In this study, the rats in the Roundup group were given 135mg/kg of Roundup daily. After converting that to a human dose, we find that it is the equivalent of a human consuming 21.9 mg/kg daily. That is an insanely high dose. The exact allowable daily intake (ADI) for glyphosate (i.e. Roundup) varies by country, but it is much lower than that. In Australia, for example, it is 0.3 mg/kg, whereas the WHO sets it as 1 mg/kg. The dose in the experiment is also well above the levels that people are normally exposed to. Even if I want to be generous, and assume the questionable estimates put forth by the “detox project” are correct and people in the US are eating up to 3 mg/kg of glyphosate daily, the dose that the rats received is still seven times that!

To put it simply, this study is worthless because the dose is so unrealistic. Even if the authors had successfully demonstrated that Digeodren did something useful when faced with those levels, that would not in any way shape or form indicate that it does anything useful when exposed to normal levels of Roundup.

Methods: Statistics

Finally, we get to the biggest problem with this study (IMO), and this one would sink it even if it was the only thing wrong with the paper. It is a problem that I write about a lot on this blog, so you may already know where I am going with this. The problem is multiple comparisons. In technical terms, the authors failed to control the family-wise type 1 error rate. In laymen’s terms, this was a statistical fishing trip. They simply did enough comparisons that they eventually got a few that were “significant” just by chance.

I’ve previously written lengthy posts about this, but to be brief, standard statistical tests like what the authors used rely on probabilities for determining statistical significance. In other words, the report “P values” that show you the probability of getting a result of the same effect size or greater than the effect size that you observed if there is not actually an effect. It’s not technically correct, but you can think of this as the probability that you could get your result just by chance. To apply this to our study, they were looking for differences among their groups, so the P values were the probabilities of getting differences as large or greater than the differences that they observed if the treatments don’t actually cause a difference. To actually determine if something is “statistically significant” we compare it to a pre-defined threshold known as “alpha.” In biology, the alpha value is usually 0.05, so any P value less than that is considered significant. What a P value of 0.05 really means, however, is that there is a 5% chance that you could get a difference that large or larger just by chance. This is really important, because it means that you will occasional get “significant” results that arose just by chance, and we call those statistical flukes type 1 errors.

Following all of that, it should make intuitive sense that as you make more comparisons, the odds of getting at least one false positive increase. In other words, if you do enough comparisons, you will eventually find some results that are statistically significant just by chance. So your error rate across all of your tests is actually much higher than 0.05. This is what we call the family-wise type 1 error rate, and it is extremely important. To compensate for it, you should do two things. First, at the outset of your study, you should have a clear prediction of what you expect to be true if your hypothesis is correct, and you should only make the comparisons that are necessary for testing that predication. You should not make a whole bunch of haphazard comparisons and hope that something significant comes out. Second, if you end up using multiple tests to answer the same question (e.g., does drug X work?) then you need to control the family-wise error rate by adjusting your alpha value (this is usually done through a Bonferroni correction). In its simplest terms, this makes the alpha more stringent as you increase the number of comparisons that you do.

So, how did our intrepid scientists do? In short, not well. They made a whopping 29 comparisons, only 8 of which showed any form of significance, and only 6 of which showed significance in a direction that would suggest that Digeodren does anything useful. Further, they did not control the error rate among these tests. In other words, they did exactly the opposite of what you are supposed to do. They went on a fishing trip looking for significance rather than only testing a small set of pre-defined expectations. They made so many comparisons that they got some statistically significant results just by chance. To put this another way, if I set up the exact same experiment with four groups of rats, but I did not give any of them Digeodren or Roundup, and I made the same 29 comparisons among those four groups, I would expect to get several significant results, even though I treated all four groups exactly the same. Their results are statistical flukes, nothing more.

Finally, they did not report their P values for each comparison, which means that we can’t even properly assess their results (see the note below). If they had reported a table of P values like they should have, we could do the Bonferroni correction ourselves, but since they failed to do that, we have nothing to go on.

To be clear, in most cases, the fact that an author did not control their error rate would not automatically mean that their results were statistical flukes, but it would mean that we should consider their paper untrustworthy and reject it. However, in this particular case, there is another important factor to consider. Namely, all of the existing evidence that homeopathy doesn’t work. When you consider that evidence, and the low quality of the experimental design of this particular study, the most rational conclusion is that the results are wrong rather than simply untrustworthy.

Note: If you read the paper, you will see a reference to a Bonferroni test as well as P values, but they only used those within a test rather than across tests. In other words, the tests that they were using (ANOVA and Kruskal-Wallis) make comparisons among several groups (in this case the four treatment groups) and report a single P value that tells you whether or not at least one significant difference exists among those groups. Then, if you get a significant result, you make pairwise comparisons among all of your groups and get individual P values for each comparison. So they reported the P values and controlled the error rates for those individual comparisons within each ANOVA, but I am talking about the P values across ANOVAs, because you should never even do the individual comparisons unless the ANOVA itself is significant, and if you don’t control the error rate across ANOVAs (as they didn’t), a lot of your ANOVAs will be false positives. In other words, they did 29 ANOVAs/Kruskal-Wallis tests, each of which compared four groups, and they controlled the error rates for the post-hoc comparisons of the four groups, but not for the ANOVAs themselves.

 Conclusion

In summary, this paper is riddled with problems and is little more than a steaming pile of crap. It had major conflicts of interest, was written by an author with a reputation for publishing shoddy, agenda-driven studies, it was published in a fringe journal, it made inadequate references to the relevant literature, the experimental design was sub-par and failed to incorporate blinding procedures, and (most importantly) it made an astounding 29 comparisons without bothering to control the error rate. This paper is a statistical fishing trip. The authors simply made so many comparisons that they eventually got a few that were significant just by chance. This is a common tactic that is frequently employed by pseudoscientists (and sometimes legitimate researchers as well) and you should learn to spot it.    

Posted in Nature of Science | Tagged , , , , , | 8 Comments

Don’t mistake an assumption for a fact

Carl Sagan quote extraordinary evidence claimsI want you to imagine for a minute that you have been selected for jury duty, and you are sitting in the courtroom listening to the evidence. As you watch, the prosecution calls an expert forensic scientist to the stand, and they carefully explain the facts concerning the forensic evidence, all of which point towards the defendant’s guilt. Then, the defendant’s lawyer stands up and shouts, “Objection, this witness has been paid off to lie about my client!” After a moment of shocked silence, the judge says, “That is quite a claim. What evidence do you have to support it?” The lawyer then responds simply by saying, “only someone who had been bought off would say things like that against my client.” Now, what do you think is going to happen next? Is that a reasonable defense that the judge will accept? Obviously not! The defense is making an extraordinary assumption, and it is clearly invalid to do so.

In an example like that, the problem is obvious. You can’t just make things up to dismiss facts that are inconvenient for you. Indeed, when a person’s fate hangs in the balance, we all want the arguments and evidence to be based on facts, not assumptions. Nevertheless, when it comes to science and many aspects of our daily lives (such as politics), people are often more than happy to accept assumptions, and people frequently state them as if they were facts. Therefore, I am going to provide several examples of this flawed line of reasoning, and explain why it not only doesn’t work, but often commits a logical fallacy.

The first example is probably the most common one that I encounter, and it is highly analogous to my courtroom example. I recently engaged in a Facebook debate with someone who made the bold claim that there is no empirical evidence for climate change. I responded to that assertion by providing him with multiple papers that did in fact provide empirical evidence for climate change. How do you think he responded? You probably guessed it. He simply claimed that all of the scientists involved in those studies had been paid off (just like my fictional lawyer did). That argument was clearly fallacious in my courtroom example, and it is equally flawed here. You can’t just assume that scientists have been paid off any time that they publish a result that you don’t like. You have to provide actual evidence of corruption, otherwise you are making a baseless assumption. In fact, journals actually require scientists to declare any conflicts of interests, and failure to do so is a serious offense (it can result in loss of funding, papers being retracted, etc.). So there is no need to make assumptions about conflicts of interest, because you can easily check and see if they exist. Nevertheless, this is one of the most common arguments among those who reject the results of modern science, and I constantly encounter it among anti-vaccers, those who oppose GMOs, climate change deniers, people who use alternative medicine and/or reject modern medicine, etc.

A very closely related line of attack is simply to accuse the person that you are debating of being a shill, astroturfer, etc.  Indeed, I constantly have to deal with people accusing me of having been bought off by big pharma, Monsanto, the government, lizard people, etc. In every case, however, it is a complete assumption (and a faulty one, as I actually pay money out of my own pocket to maintain this blog). You can’t just go around assuming that everyone who disagrees with you has been bought off. That’s not rational.

I could continue giving copious examples of this type of reasoning (e.g. assuming that “big pharma” has bought of the FDA and CDC), but I think that you get the basic picture, so I want to move on to explaining a bit more about why this type of argument is invalid. There are actually two logically fallacies being committed here. First, all of the specific examples that I have given so far commit a genetic fallacy (sometimes specifically an ad hominem fallacy). These fallacies occur when you make an irrelevant or unjustified attack against the source of the information, rather than addressing the information itself (even a flawed source can sometimes be correct). I have previously talked about these fallacies at length (see previous hyperlinks), so I want to focus instead on the second fallacy. This one is more general and can occur even when a source is not being attacked. It is what is known as an ad hoc fallacy. It generally occurs as a response to an argument, rather than an argument itself, and it has the defining characteristic of proposing a solution that you would never accept unless you were already convinced by the claim that was being defended. In other words, an ad hoc fallacy is a response that is not based on evidence and whose sole purpose is to “solves” a problem in an argument that you are fond of.

That may have seemed a bit complicated, so let me explain by applying it to one of the examples that I used previously. In the example of the climate change denier, his claim was that there is no empirical evidence of climate change (that is the position that he was defending). I presented him with actual evidence, thus clearly contradicting his claim. He then responded to that evidence by asserting that all of those scientists were really paid shills. However, there is no evidence to support that assertion, and there is no reason to think that the assertion is true other than a desire to believe the initial claim. So he was simply assuming that the scientists had been paid off, even though he had no evidence to support that claim. Do you see how this works? It is an assumption that is stated as if it was a fact, but there is no actual reason to think that the assumption is true. It serves no function other than patching the hole in the argument, and you wouldn’t accept it unless you were already convinced that the initial claim was true.

Nevertheless, not all assumptions are ad hoc fallacies, but that doesn’t make them any less flawed when they are used in debates. You simply cannot state assumptions as if they are facts. The hidden cancer cure conspiracies provide a great example of these, because they involve assumptions stacked on other assumptions. In order to argue that pharmaceutical companies are hiding a cure for cancer, first have to assume that a cure actually exists, but that is a meritless assumption. Can you provide me with actual evidence that pharmaceutical companies have a cure hidden on their shelves? No, you can’t, which means that you don’t get to state the claim that they have a cure as if that claim is an evidence-based fact. Similarly, you also have to assume a rather large conspiracy in which everyone involved is willing to sit back and watch their friends and family members die. This conspiracy would have to involve not just the CEOs of companies, but also people on the ethics committee who approved the research, people in the FDA who approve the trials, accountants who handled the budgets, lab techs, the scientists themselves, etc. That’s an awful lot of people to be involved, and you cannot just go around inventing conspiracies like that without providing actual hard evidence of their existence. This conspiracy theory (and the vast majority of conspiracy theories) relies heavily on unsubstantiated assumptions, which is a big part of why it is irrational.

Indeed, if it was permissible to substitute assumptions for facts, then we could all do this anytime that we wanted.  I could, for example, claim that smoking actually has health benefits, and the tobacco industry scientists were actually correct about the safety of cigarettes, but big pharma paid off a ton of scientists to falsify data showing that cigarettes were dangerous because big pharma wanted to profit off of sick people. Now, you probably think, “that’s ridiculous!” and indeed it is, but it is no more ridiculous than claiming that pharmaceutical companies paid off scientists to falsify information on vaccines, claiming that marijuana is only illegal because legalizing it would hurt pharmaceutical companies, claiming that climate scientists are falsifying data, claiming that Monsanto controls the world’s agriculture scientists, etc. All of these are baseless assumptions, and you cannot present them as if they are facts.

As I approach the end of this post, I want to remind everyone of one of the most important concepts in debates and rational thinking: the burden of proof. This states that the person making the claim is always required to provide legitimate evidence to support it. In other words, if you want to claim that scientists have been paid off, then it is your duty to provide actual evidence to support that claim, and if you cannot do that, then you are stating an assumption, not a fact, and your argument is illegitimate. Similarly, if you want to claim that companies are hiding cures, a conspiracy is afoot, etc., you must provide evidence to substantiate those claims. You simply cannot assume things that haven’t been verified, because if you could, then we could all dismiss every single argument that we don’t like simply by assuming the existence of some contrary evidence. Also, it is worth explicitly stating that you have to show the evidence, not the other way around. In other words, if you are claiming that a conspiracy exists, you have to provide evidence that it exists, whereas I do not have to provide evidence that it doesn’t exist. That’s the way that the burden of proof works. In fact, saying “you can’t prove that it doesn’t exist, therefore it is valid to think that it does exist” is a logical fallacy known as an argument from ignorance.

Finally, although I have been speaking specifically in the context of debates, you really should apply this to your own views (regardless of which side of a topic you stand on). Think through your arguments and make sure that you can provide proper evidence from high-quality sources to back up each component of your reasoning. Seek out your assumptions, test them against the evidence, and if they cannot be verified or strongly supported, reject them. Admitting that, “I don’t know” is far, far better than insisting that you do know, when in fact you are simply making an assumption.

Note: I fully expect someone to read this and say, “but we use assumptions all of the time in our daily lives and they are useful.” That is true, but it is irrelevant for several reasons. First, the assumptions that we make in our daily lives are very often flawed and frequently get us into trouble. They are, in fact, unreliable, which is why they aren’t valid substitutes for evidence and can’t be used in scientific debates. Second, many of the things in daily life that we often call “assumptions” are actually evidence based conclusions. For example, if my PhD adviser emails me and asks me to meet him in his office at 3:00, I am going to “assume” that he means 3:00PM, but that is not really an assumption (at least not in the way that I have been using the word in this post). Rather, it is a probabilistic conclusion based on evidence. I say that, because every single time that my adviser has ever asked to meet with me, it has been a meeting during daylight hours. Thus, there is no rational reason to think that he actually means 3:00AM. Situations like that are clearly extremely different from creating conspiracy theories or assuming corruption just because you don’t like what a scientist published.

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Debunking the creationist myth that mutations don’t produce new and useful information

Genetic variation is a fundamental requirement for evolution, but many of the evolutionary mechanism (such as selection and genetic drift) actually remove variation from populations. Therefore, evolution is entirely reliant on the formation of new genetic information, and without it, evolution would grind to a halt. Creationists often seize this fact, and erroneously claim that evolution is impossible because we have never found a mechanism that is capable of creating new genetic information. This claim is, however, completely false, because mutations do, in fact, create new genetic information. Nevertheless, many creationists respond to that fact by insisting that mutations simply “rearrange” existing genetic information, rather than creating “new” information. Therefore, I want to briefly explain why this argument is fundamentally flawed.

Hopefully everyone recognizes this, but if not, it's Mr. DNA from Jurassic Park

Hopefully everyone recognizes this, but if not, it’s Mr. DNA from Jurassic Park

Before I can explain the problems with this argument, we need to be clear about the basics, and you need to have at least a rudimentary understanding about how DNA works. DNA (or deoxyribonucleic acid if you prefer) consists of four bases adenine (A) guanine (G) thymine (T) and cytosine (C). These four bases get arranged into groups of three, and each group of three codes for an amino acid. Those amino acids then get strung together to form a protein, and those proteins combine to form tissues. Thus, your DNA is your body’s blue-print, and it tells your body which amino acids to make, and how to combine them to make proteins, tissues, organs, etc.

Please note that although what I have presented here is the most fundamental concept for you to understand, actual DNA also includes stop codons, start codons, and many other complexities that are irrelevant for this post.

 Now that you understand the basics of DNA, we can talk about mutations. I explained them in far greater detail here, but in short, they are simply random changes to the genetic code (the ones that are important for evolution usually occur during the formation of egg and sperm cells). There are many different types of mutations such as inversions (which flip a segment of DNA), deletions (which remove base pairs), insertions (which add extra base pairs), substitutions (which insert the wrong base into a chain [e.g. a T instead of a C]), duplications (which duplicate a segment of DNA), and several others. In every case, however, they change the genetic code, and by changing the code, they can change the amino acids and ultimately the proteins that are produced (note: there is redundancy in the way that amino acids are coded, so not all mutations result in changes downstream).

At this point, we can examine creationists’ claim that mutations simply rearrange information rather than producing new information. The most obvious problem with this is simply that some mutations (like insertions) do actually insert entirely new base pairs. In other words, they don’t rearrange the code that is already there. Rather, they add new bases (i.e., new information) to that code.

The second problem is really the more important and fundamental one. Namely, this argument seems to be using the word “new” in a rather peculiar sense, because rearranging the existing bases does in fact produce a new code that often results in the production of different amino acids and new proteins. Let me illustrate. A coding strand of DNA that contains the sequence CTT would code for the amino acid Leucine. However, if a mutation rearranged those three bases so that they were TCT, that strand of DNA would code for the amino acid Serine instead of Leucine. Thus, by simply rearranging the existing bases, we created new information which produced a different amino acid. In other words, we added genetic variation to the population, because the individuals who receive that mutation will produce Serine, while the result of the population is producing Leucine. Objecting to that by claiming that “no new information has been created” is really quite silly because the fact remains that individuals with that mutation are producing a different amino acid than everyone else. Something that codes for the production of an entirely different amino acid is, by any reasonable definition, “new information” (i.e., it is information that was not there before).

A useful analogy to help you conceptualize this is to think about letters in the alphabet. The English language has 26 letters (bases), and we combine those letters to from words (amino acids). We then arrange those words into sentences (proteins), and we arrange those sentences into paragraphs (tissues). Ultimately, we can use those paragraphs to make books, essays, etc. (organisms). Now, according to creationists’ reasoning, it should be impossible to make any new information by simply rearranging those 26 letters, but that is clearly absurd. We can arrange them one way and produce the works of Shakespeare. We can arrange them another way and produce “The Origin of the Species.” We can rearrange them yet again and produce the script to a Stargate episode. Indeed, there are a virtually infinite set of possibilities, each of which contains different information, and the situation is no different for DNA. We can arrange the bases one way and get a dinosaur, and we can rearrange that code (via mutations) and get a chicken. We can rearrange it yet again and get a whale, human, tree, bacteria, mushroom, etc. This notion that making new arrangements of the four existing bases doesn’t produce new information is absurd because everyone agrees that different arrangements of those bases produce very different organisms.

A big part of the problem here once again comes back to the definition of the word “new.” Creationists seem to think that evolution requires something that is completely and totally novel, such as a new base pair or, at the very least, an entirely new amino acid that has never existed anywhere before, but that is a straw man fallacy. Evolution does not require something that has never previously existed anywhere. Rather, it simply needs to have variation. Thus, any change to the genetic code is “new information” in an evolutionary sense, because it provides variation. A useful way to think about this is that evolution doesn’t need “new” information. Rather, it needs “different” information. In other words, all that it needs is a code that is different than the one that was there before.

To further illustrate what I mean by this, it is worth mentioning that even mutations that remove bases can actually produce new information in an evolutionary sense. Going back to the alphabet example, imagine that the document in question is a recipe, and imagine that the instructions get “mutated” by the random deletion of one of the steps. That actually provides new information because the end-product will be something different than the intended product (i.e., there will be variation for the trait). The same thing happens with DNA. If you remove the bases for a particular amino acid, then the final protein product will often be different from the one that was originally coded for, and that variation is all that evolution needs, regardless of whether or not you want to describe it as “new.” To give an actual example of this, the virus that causes HIV typically uses the CKR5 protein to enter macrophages and complete its lifecycle. However, people who have deletions (a type of mutation that removes bases) on both copies of their genes for CKR5 are resistant to HIV because those mutations alter the protein, and HIV cannot bind to this new, modified protein (Dean et al. 1996; Sullivan et al. 2001). Thus, a loss of genetic material results in a new set of instructions, which causes cells to produce a protein with a new modification, and that new modification provides a beneficial function. Creationists may try to say that this example doesn’t illustrate the formation of “new” information because an existing protein was simply modified, but that is, once again, a straw man fallacy, because evolution just needs variation, and this mutation provides that (thus, from an evolutionary perspective, it is new information).

Finally, you might be tempted to protest to all of this on the grounds that, “mutations are nearly always harmful,” but that claim is a myth. Most mutations are actually neutral at the time that they occur (i.e., they are neither beneficial nor harmful; Nachman and Crowell 2000; Eyre-Walker et al. 2007). Further, although harmful mutations certainly do exist, they are selected against, so they are really irrelevant. In other words, even if there were 10,000 times as many harmful mutations as beneficial mutations (which there aren’t), that wouldn’t preclude evolution, because nature would select against the harmful ones and for the beneficial ones. On that note, I want to be absolutely, 100% clear that beneficial mutations do exist and have been well documented (Newcomb et al. 1997; Dean et al. 1996; Sullivan et al. 2001; Shaw et al. 2002, 2003; Joseph and Hall 2004; Perfeito et al. 2007; see Halligan and Keightley 2009 for a good review). Indeed, we have done experiments with bacteria were we monitored populations for many generations, and observed the formation of novel, beneficial mutations that provided the bacteria with new information that allowed them to perform a novel function that they were previously incapable of (Blount et al. 2008; more detailed explanation in the citrate section of this post).

From the Star Trek TOS episode "I, Mudd"

From the Star Trek TOS episode “I, Mudd”

In short, this notion that mutations can’t produce new genetic information is laughably absurd. DNA is simply the code that tells organisms what proteins and structures to make, and modifications to that code result in novel modifications, proteins and structures. Indeed, if you accept that dogs and cats are different because of differences in their DNA, then you have already accepted that rearranging genetic codes can in fact produce new information and result in vastly different organisms. Indeed, saying that rearranging DNA can’t produce new information is no different from saying that rearranging the letters of the alphabet can’t produce new information. Obviously it can, as is evidenced by this new blog post that I am writing. Additionally, when we say that evolution needs “new information,” we don’t mean that it needs something completely novel and totally different from anything else that has ever existed. Rather, we simply mean that there needs to be variation for traits. In other words, any slight modification to an existing trait qualifies as “new information” when talking about evolution. Finally, we have experimentally documented that mutations can produce that variation, and we have demonstrated that beneficial mutations do in fact occur and result in novel proteins that perform novel functions (i.e., the mutations created new information).

Literature Cited

Posted in Science of Evolution | Tagged , , , , | 12 Comments

Don’t attack the straw men: Straw man fallacies and reductio ad absurdum fallacies

strawmanPeople love to argue. We all have views and opinions, and we tend to promote them prominently and viciously attack opposing ideas. There is nothing inherently wrong with that as long as your views are evidence-based and you use proper logic when attacking your opponent’s position; however, many people fail at this and succumb to logical fallacies. One of the most common blunders is something known as a straw man fallacy. This occurs anytime that you misrepresent your opponent’s argument, then attack that misrepresentation instead of the view that they actually hold. It is a fairly simple concept, but it is often misunderstood, and it is rampant in debates (this year’s presidential election has been full of a sickening number of these fallacies). Therefore, I want to talk a bit about this fallacy and when it does and does not occur, as well as explaining a particular subset of straw man fallacies known as reductio ad absurdum fallacies.

 

Straw man fallacies

Let’s begin with the basics, what are straw man fallacies? To put it simply, they are distortions of an argument that usually present a weak and easily defeated version of the actual argument. In other words, one debater will claim that their opponent believes view X (which is a distorted and weakened version of what their opponent actually believes), then they will explain why X is wrong. The problem with this should be obvious. If the opponent does not actually believe X, then showing that X is wrong does nothing to address the opponent’s actual beliefs. In other words, it doesn’t matter if X is wrong if X isn’t actually what your opponent is claiming. Nevertheless, this fallacy can be an extremely persuasive (albeit invalid) debate tactic that many people are duped by.

On that note, it is worth mentioning that although straw man fallacies can be deliberate, and many people use them with the intention of deceiving their audience, they can also occur unintentionally. This usually happens when someone is ignorant about the topic that they are debating, and I frequently encounter these arguments when talking to people who reject scientific results. For example, one of the most common creationist arguments is, “if we evolved from monkeys, then why are there still monkeys?” This is a straw man fallacy because evolution does not state that we evolved from monkeys (or even great apes). Rather, it states that we share a common ancestor with them. Thus, by making this argument, creationists are not in any way shape or form presenting a legitimate criticism of the theory of evolution, because they are attacking a claim that evolution does not actually make. Similarly, I often encounter religious people who say that climate change can’t be true because their religion says that the earth won’t be destroyed, and climate change says that it will be destroyed. If you actually understand climate change, however, then the problem with that line of reasoning is obvious. Namely, climate change does not claim that we are going to destroy the earth. Climate change is a serious problem, but it won’t cause our extinction.

I wanted to use those two examples not to attack creationists and climate change deniers, but rather to illustrate an important point: you need to understand a given topic before you decide whether or not to accept it. Otherwise, your arguments will often be straw men fallacies, and they will make your opponents think that you are ignorant, rather than making them actually consider your position. Further, this is important for far more than just winning debates. I personally care more about knowing what is true than I care about winning a debate, but if I have not even bothered to learn the fundamental concepts of the opposing position, then I can’t have any confidence in my conclusions. You need to actually study a topic thoroughly, before you reach a conclusion, and defiantly before you try to debate someone on it.

What isn’t a straw man fallacy

When it comes to the internet articles, public debates, and other venues where someone is not specifically debating you, do not assume that someone is committing a straw man fallacy just because they did not address a specific argument that you personally think works. In other words, if they attacked an argument that essentially no one actually uses, then they committed a straw man fallacy. However, if they attacked an argument that many people use, then they did not commit a fallacy even if you do not personally use that argument.

Let me give you an example of what I mean. Last week, I wrote a post debunking 25 common arguments against climate change (mostly arguments that climate change isn’t happening or we aren’t causing it). All 25 of them are arguments that I personally encounter frequently when debating people. Nevertheless, some people were quick to accuse me of committing a straw man fallacy, and they did so based on the grounds that they personally accept that we were causing climate change, but simply debate the amount of change that will happen (which is not a line of reasoning that I addressed). So, did I commit a straw man fallacy? No! Every argument that I addressed is an argument that many people actually use. The fact that some people have arguments that I did not address does not make the arguments that I did address fallacious. I cannot predict the argument that every single reader of my blog will use.

To be clear, if I had made grand, generalizing claims like, “everyone who debates climate changes believes these arguments” or “these are the only arguments against climate change,” then I would have committed a straw man fallacy. Similarly, if I was actually directly debating one of these people, and they said, “I accept that climate change is true, but I disagree about its extent” and I responded by providing them evidence that it was true, then I would have committed a straw man fallacy, because I would not actually be addressing the argument that they had made to me. I cannot, however, be held responsible for failing to predict every single argument that anyone anywhere would ever make.

A similar example frequently occurs with anti-vaccers. I often write and share posts about vaccine effectiveness, and almost every time that I do, I get some angry anti-vaccer yelling at me with statements like, “This is such nonsense. The issue is about whether or not vaccines are safe, not whether or not they work!” As with the climate change arguments, however, there certainly are people who accept that vaccines work but erroneously think that the costs outweigh the benefits; however, there are also many people who do, in fact, deny that vaccines even work. So unless I am specifically addressing a group of people who are arguing about safety (rather than effectiveness), there is nothing fallacious about discussing vaccine effectiveness, because many people do actually argue that vaccines aren’t effective.

 

Reductio ad absurdum fallacies

At this point, I want to shift gears slightly and talk about another type of logical fallacy that is really just a special case of the straw man fallacy: reductio ad absurdum. That may sound like a Harry Potter spell, but it is actually a logical fallacy that occurs when you take a position, stretch it to an absurd conclusion that would not actually be supported by the original statement, then claim that the original statement must be wrong because the conclusion is clearly absurd. That may have sounded complicated, so let me give you a few examples.

On several occasions I have shared posts which explain that most people don’t need to take extra vitamins and dietary supplements because they already get a sufficient amount from their diet and their body can’t really utilize excess amounts. Whenever I share these posts, however, I almost invariably get responses like, “You’re such an idiot! You claim to be a scientist and you don’t even know that vitamins are important!? You would die without them!” Let’s think about this for a second. Did I claim that vitamins aren’t important or that your body doesn’t need them? No, I didn’t even imply it. There is a huge difference between saying that you don’t need to take excess vitamins and saying that you don’t need any vitamins. In other words, the argument that I presented states that most people in industrialized countries already get the vitamins that their bodies need from their diets, and they don’t need to take extras. Internet trolls then took that argument and stretched it to the absurd conclusion that vitamins weren’t necessary at all, then accused me of being an idiot based on that clearly absurd conclusion. Do you see the problem? The conclusion that they presented was based on a distortion of my argument, rather than the argument itself.

To give one more example, on several occasions I have shared posts that explain why juice cleanses and “super foods” can’t actually boost an already healthy immune system, and the wonderful people of the internet usually respond by asserting that it is obvious that a healthy diet is important and your immune system won’t function well if you’re malnourished. As with the vitamin argument, however, I never asserted that a healthy diet isn’t necessary. I was talking about boosting an immune system above its normal functioning levels, not basic nutrition. In other words, saying that you can’t boost a healthy immune system is not the same thing as saying that you can eat nothing but junk and expect to be healthy.

 

Reductio ad absurdum logic

This illustrates the correct use of reductio ad absurdum logic. The second stick figure is sarcastically illustrating that if the argument that science has been wrong in the past actually invalidated a current scientific result, then we could use that argument anytime that we wanted, but that would obviously lead to absurd conclusions. Note: sarcasm is not a requirement for reductio ad absurdum logic, but it is often included.

This illustrates the correct use of reductio ad absurdum logic. The second stick figure is sarcastically illustrating that if the argument that science has been wrong in the past actually invalidated a current scientific result, then we could use that argument anytime that we wanted, but that would obviously lead to absurd conclusions. Note: sarcasm is not a requirement for reductio ad absurdum logic, but it is often included.

Finally, it is important to note that reductio ad absurdum logic can actually be applied without committing a fallacy if you can show that the actual argument that your opponent is using would lead to an absurd conclusion if it was applied consistently. As long as you do not distort the original argument, then this technique is not only valid, but it is extremely powerful (it is one of my favourite tools).

Let me give you an example. I often encounter people who say things like, “all that I need to know about climate change is that Al Gore thinks it is happening. If he thinks that it is true, then it must be wrong!” This argument is technically a guilt by association fallacy, but we can easily demonstrate the flaw in it by using reductio ad absurdum logic. In this case, I usually counter this claim by pointing out that Al Gore also thinks that we are breathing oxygen, so if we use this argument consistently, then we must conclude that we are not in fact breathing oxygen. Do you see why that response works? I did not distort the argument, rather I showed that it actually would lead to an absurd conclusion if it was a good argument. I can prove this by setting up two identical syllogisms.

Original argument:

  1. If Al Gore thinks that something is true, then it must be wrong
  2. Al Gore thinks that climate change is true
  3. Therefore, climate change is wrong

Analogous argument using reductio ad absurdum logic:

  1. If Al Gore thinks that something is true, then it must be wrong
  2. Al Gore thinks that we are breathing oxygen
  3. Therefore, we aren’t breathing oxygen

See how this works? I simply took the original argument, applied it to a different topic, and showed that if we apply that argument consistently, we arrive at an absurd conclusion. I provided many more examples of this debate tactic in this post on consistent reasoning, so please see it if you are confused. You should also watch John Oliver, because he wields this logical tool brilliantly (sometimes he does also slip into reductio ad absurdum fallacies, but that is generally to set up a joke rather than make a serious argument).

 

Conclusion

In short, straw man fallacies are simply distortions and misrepresentations of your opponent’s argument. They can be intentional or unintentional, but they are easy to avoid by simply being well-informed on the topic that you are debating. Nevertheless, many people continue to use them and incorrectly accuse other people of using them. Additionally, these fallacies contain a special subset of fallacies known as reductio ad absurdum fallacies. These occur when an argument is stretched to an absurd conclusion that is not supported by the original argument. Although that strategy is fallacious when the argument is distorted in the process, it can also be a very powerful debate tool if you can demonstrate that the original argument itself actually leads to an absurd conclusion when it is applied consistently.

Note: I want to be clear that on topics like vaccines, climate change, evolution, etc. there really aren’t “two sides.” So I when I say that you need to thoroughly study the topic before reaching a conclusion, I am not suggesting that you need to read a bunch of conspiracy blogs, creationist websites, etc. Rather, you need to study the peer-reviewed literature (including the handful of studies that disagree with the consensus). You don’t need to read unreliable sources in order to be well-informed. However, if you want to actually debate people about these topics, then you really should spend time studying those unreliable sources, because if you don’t, you will often end up committing straw man fallacies. Indeed, I have seen my fellow skeptics do that on several occasions (and I’ve probably unknowingly done it myself at some point).  

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Posted in Rules of Logic | Tagged , , , | 11 Comments

Debunking 25 arguments against climate change in 5 sentences or less (each)

Climate change is arguably one of the most misunderstood and controversial topics among the general public. Misinformation abounds, and many people are left debating whether or not we are causing it, and even whether or not it is happening at all. Among scientists, however, there is no serious debate, and there hasn’t been for many years. The evidence for climate change is extremely solid, despite what many blogs and politicians will tell you. Therefore, I want to try to correct some of that misinformation. Yesterday, I posted an extremely lengthy article debunking 25 myths and bad arguments about climate change. Today, I am posting the same information, but in a much more condensed form. I have attempted to address each argument in under 5 sentences. Obviously I had to leave out a lot of information, so if you want the more detailed explanations, please see the original post (each short response is accompanied by a link for the full-length explanation).

 

stephen-colbert-global-warmingBad Argument/Myth #1: It snowed, so global warming must not be true

Reality: Climate and weather are not the same thing. Climate change predicts that on average the earth’s temperature will increase ,but that does not mean that it will always be hot everywhere or that it will never snow. This is a straw man fallacy/reductio ad absurdum fallacy.

click here for the full version of #1

Bad Argument/Myth #2: The ice in Antarctica is actually increasing

Reality: This argument is a Texas sharpshooter fallacy because it focuses on one ice shelf and ignores the fact that Arctic sea ice has declined substantially (Stroeve et al. 2015), glaciers are rapidly retreating all over the world (WGMS 2013), sea levels are rising (Yi et al. 2015;  NOAA), temperatures are increasing, etc. Also, it is important to note that climate change does not predict that every part of the earth will be warmer all of the time. The average temperature is increasing, but that does not mean that every single spot will be warmer.

click here for the full version of #2

This shows the temperature data once the effects of El Ninos, solar fluctuations, and volcanoes. Image via Open Mind.

This shows the temperature data once the effects of El Ninos, solar fluctuations, and volcanoes. Image via Open Mind.

Bad Argument/Myth #3: Global warming has paused

Reality: Not it hasn’t. To make this claim you have to cherry-pick your data set, cherry-pick your years, and ignore the confounding factors, none of which is logically or scientifically valid. Indeed, the actual scientific analyses of the data show that warming has not paused at all (Easterling and Wehner 2009; Santer et al. 2011; Karl et al. 2015; Lewandowsky et al. 2015a,b), especially when you look at the oceans (Balmaseda et al. 2013; Rhein et al. 2013; Glecker et al. 2016) and account for confounding variables (Foster and Rahmstorf 2011).

click here for the full version of #3

Bad Argument/Myth #4: Global warming wasn’t happening so they changed to name to climate change

Reality: Scientists have been talking about climate change since day one. They changed the name because the term “global warming” is misleading and was leading to faulty arguments (like #1 and 2). The planet is warming on average, but climate change is also about shifts in rainfall patterns, sea level changes, ocean current changes, etc., not just warming.

click here for the full version of #4

Bad Argument/Myth #5: The models have all been wrong

Reality: No they haven’t. They have actually been quite accurate (Hansen et al. 2006; Frame and Stone 2012; Rahmstorf et al. 2012; Cowtan et al. 2015;  Marotzke and Firster 2015). Claims to the contrary are based on a misleading and deceptive distortion of statistics.

click here for the full version of #5

Bad Argument/Myth #6: Polar bear numbers are actually increasing!

Reality: No they aren’t. They are decreasing (Schliebe et al. 2006; Stirling and Derocher 2012). Also, it’s not just that the populations are declining. Rather, cub mass is going down, juvenile recruitment is going down, body condition is going down, etc. (Rode et al. 2010, 2012). All of these factors are because the habitat that they need is vanishing, which means that they can’t find enough food, can’t raise proper-sized young, and will ultimately disappear from much of their current range if climate change isn’t halted (Hunter et al. 2010; Molnar et al. 2011).

click here for the full version of #6

Bad Argument/Myth #7: The climate has changed in the past, so the current warming is natural. It’s the sun, volcanoes, Milankovitch cycles, etc.

Reality: The fact that climate changed naturally in the past only tells us that it is possible for the climate to change naturally. It does not indicate or even suggest that the current warming is natural. Scientists have carefully examined the sun, volcanoes, Milankovitch cycles, etc., and none of them can explain the current warming trend (Meehl, et al. 2004; Wild et al. 2007; Lockwood and Frohlich 2007, 2008; Lean and Rind 2008; Foster and Rahmstorf 2011; Imbers et al. 2014). When you add anthropogenic greenhouse gasses into the statistical models, however, you get a tight match between the observed and expected values (Stott et al. 2001; Meehl et al. 2004; Allen et al. 2006; Lean and Rind 2008; Imbers et al. 2014; more details here).

This figure from Hansen et al. 2005 shows the effect of both the natural and anthropogenic drivers of climate change. Notice how only anthropogenic sources show a large warming trend. Also, see figure 2 of Meehl et al. 2004.

This figure from Hansen et al. 2005 shows the effect of both the natural and anthropogenic drivers of climate change. Notice how only anthropogenic sources show a large warming trend. Also, see figure 2 of Meehl et al. 2004.

click here for the full version of #7

Bad Argument/Myth #8: During past climate changes, the CO2 follows the temperature increase

Reality: This is only true at first. What happened in the past was that a small amount of warming (usually regional) from factors other than CO2 (such as Milankovitch cycles) caused the oceans to warm up and release the CO2 stored in them (Martin et al. 2005; Toggweiler et al. 2006; Schmittner and Galbraith 2008; Skinner et al. 2010). Then, that increase in CO2 caused the majority of the warming (Shakun et al. 2012). So CO2 was actually the major driver of past climate changes (Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012).

click here for the full version of #8

Bad Argument/Myth #9: CO2 only makes a small portion of the atmosphere

Reality: The fact that something is not abundant does not mean that it is not important. Indeed, that tiny percentage of CO2 is the difference between our nice warm world and an inhospitably cold world, and we know that past climate changes have been largely driven by CO2 levels (Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012). Additionally, satellites have provided direct empirical evidence that the earth is currently trapping more heat than it used to, specifically at the frequencies that are absorbed by CO2 (Harries et al. 2001; Griggs and Harries 2007).

click here for the full version of #9

Bad Argument/Myth #10: We only emit a tiny portion of the earth’s CO2

Reality: Before us, the system was in balance, with roughly the same amount being removed and produced (plants, the ocean, etc. all remove some CO2). Thanks to humans, however, that balance has shifted and now more CO2 is being produced than is being removed. As a result, CO2 levels have increased rapidly since the start of the industrial revolution, and they are currently at their highest point in past 14–16 million years (Tripati et al. 2009). Also, we have verified that the increased CO2 is from us via changes in carbon isotope ratios (Bohm et al. 2002; Ghosh and Brand 2003;Wei et al. 2009; details here).

click here for the full version of #10

Bad Argument/Myth #11: Water vapor is a far more potent greenhouse gas than CO2

Reality: Water vapor increases in response to an increase in temperature. So, water vapor is a feedback mechanism, wherein CO2 from us causes some warming, that warming increases the amount of water vapor in the atmosphere, and that water vapor causes even more warming (Held and Soden 2000; Philipona et al. 2005). So ultimately, the warming is still from us producing CO2.

click here for the full version of #11

Bad Argument/Myth #12: In the 70’s scientists predicted an ice age

Reality: No they didn’t. There were a total of seven papers on global cooling, and during that exact same time, 42 papers were published on global warming (Peterson et al. 2008). The media may have said that we were entering an ice age, but that was never what the majority of scientists were saying.

click here for the full version of #12

Bad Argument/Myth #13: It’s just a theory, not a fact

Reality: In science, the difference between a theory and a fact has nothing to do with our certainty. Rather, a fact is a single observation, result, etc. whereas a theory is a broad and rigorously-tested explanatory framework that both explains the facts and allows us to make predictions about what future experiments should show. For example, if I drop a pen, then I have just demonstrated the fact of gravity (i.e., it is a fact that gravity caused my pen to drop), but the theory of universal gravity, explains that fact by stating that all bodies that have mass produce gravity and are acted upon by the gravity of other bodies.

click here for the full version of #13

Bad Argument/Myth #14: But scientists have been wrong in the past, and we can’t be totally certain that climate change is true

Reality: The fact that scientists have been wrong before and might be wrong now does not mean or even suggest that they actually are wrong now. You have to present actual evidence that they are wrong now, otherwise this is an argument from ignorance fallacy. Indeed, if this argument worked, you could use it anytime that you wanted. For example, you could say, “scientists say that gravity is true, but scientists have been wrong before and we can’t be totally sure that gravity is true, so I don’t have to accept them now.”

click here for the full version of #14

scientific consensus on global climate change, global warming

Image via James L. Powell. More details on the scientific consensus here.

Bad Argument/Myth #15: There are thousands of scientists who disagree (e.g., the Oregon Petition)

Reality: The overwhelming majority of climatologists (somewhere in the high 90’s) agree that we are causing climate change. The Oregon petition is a fraud. Most of its signatories weren’t real scientists, and only 39 of them were climatologists. Also this argument is a blatant appeal to authority fallacy.

click here for the full version of #15

Bad Argument/Myth #16: “Climategate” showed that scientists are falsifying data

Reality: It did no such thing. The stolen emails were full of conversations about real data, but a handful were taken out of context and twisted to make them appear corrupt. In context, however, nothing unethical was occurring. Indeed, the situation has been examined by multiple different independent investigations (including the National Science Foundation, US Environmental Protection Agency, UK House of Commons Science and Technology Committee, Pennsylvania State University, and University of East Anglia), and all of them concluded that there was no evidence that the scientists were manipulating data, involved in a conspiracy, etc.

click here for the full version of #16

Bad Argument/Myth #17: Scientists are manipulating the data to make it look like warming!

Reality: No they aren’t. Almost all real data sets have to be adjusted for biases in the collection methods, and climate change data are no different. The data have been collected over many years using different methods, and different methods have different biases. Therefore, the only way to use all of those data sets is to adjust for those biases in methodologies (details here and here). Scientists have been open about these corrections and have publicly documented them from day one (details here and here).

click here for the full version of #17

global warming money

Details and sources here.

Bad Argument/Myth #18: It’s a liberal conspiracy/It’s all about the money!

Reality: These are ad hoc fallacies (i.e., unless you provide actual evidence that they are corrupt, these claims are baseless assumptions). Additionally, if we are going to go down this road, then let’s flip things upside down and ask the opposite question: who would benefit from opposing climate change research? The answer to that question is pretty obvious: oil companies. If scientists could really be bought off so easily, then why haven’t multi-billion-dollar oil companies been able to buy off more than a handful of them? Given the vast wealth of oil companies, the millions of dollars that they have poured into denialist organizations, and the economically unstable state of most governments, surely oil companies could offer scientists more than governments could.

click here for the full version of #18

Bad Argument/Myth #19: But politicians and the media…

Reality: They are irrelevant. I don’t give a crap what politicians and the media think or say. Both of them are repeatedly wrong about the science (on both “sides” of the topic). So I don’t care what Al Gore said or thinks, I don’t care what erroneous claims CNN has made, etc. I care about the science, and the scientific evidence overwhelmingly shows that climate change is happening and it is our fault. Using politicians and the media to attack science is a guilt by association fallacy, because what they think, say, and do is completely, 100% irrelevant to whether or not the science is correct.

click here for the full version of #19

Bad Argument/Myth #20: Climate change is being caused by the ozone hole (or vice versa)

Reality: Climate change and the ozone hole are separate phenomena that sometimes interact. Climate change is caused by greenhouse gasses trapping heat before it leaves the earth, whereas the ozone hole is caused by chlorofluorocarbons and other gases that deplete the ozone, thus allowing high levels of UV radiation to enter the earth. They are caused by separate sets of gases, and they act very differently.

click here for the full version of #20

Bad Argument/Myth #21: But CO2 is actually good for plants

Reality: It is true that increased CO2 levels will generally result in more plant growth, but that relationship is complicated (Robinson et al. 2012). Indeed, there are lots of other factors to consider, such as changing precipitation patterns, which are often very harmful to plants (Allen et al. 2010; Carnicer et al. 2011). So many plants will actually be negatively impacted. Finally, this argument is really quite irrelevant, because even if plants would universally benefit from increased CO2, that wouldn’t mitigate the sea level rise, increased heat waves, etc. (see #22). In other words, the CO2 levels are increasing, so the plants clearly can’t keep up (i.e., plant growth isn’t increasing fast enough to balance out the CO2 that we are producing; see #10).

That was the full version for this one.

Bad Argument/Myth #22: It’s not really a big problem because the planet will only warm by a few degrees

Reality: Even a slight increase in temperature will have huge consequences. Indeed, we are already seeing the changes. Glaciers and ice caps are melting (WGMS 2013; Stroeve et al. 2015), the sea is rising (Yi et al. 2015), animals and plants are shifting their ranges and behaviours (Root et al. 2003; Tingley et al. 2012), forests are being affected (Allen et al. 2010; Carnicer et al. 2011),  heat waves and heat associated mortalities are increasing (Patz et al. 2005; Luber and McGeehin 2008; Kuglitsch et al. 2010), extreme weather events are increasing (Coumou and Rahmstorf 2012), coral reefs are bleaching (Hoegh-Guldberg and Bruno 2010), droughts are increasing (Dai 2013), etc., and all of these things will only get worse with time. In many parts of the world, it will be harder to grow crops (Schlendker and Roberts 2009), coastal properties will be lost, diseases will expand their ranges, etc.

click here for the full version of #22

Bad Argument/Myth #23: It will make humans go extinct/it will be the end of the world

Reality: Following #22, it is important to clarify that although climate change is a very serious problem that will make life on earth more difficult, it is not going to end life as we know it. The media loves to sensationalize things, but very, very few professional climatologists think that it will end the world.

click here for the full version of #23

Bad Argument/Myth #24: God is in control

Reality: If you actually believe this and think that climate change isn’t a problem because it won’t happen unless God allows it, then you should never take action on anything. If, for example, you see that a child is about to get hit by a bus, there is no point in trying to save him because the bus won’t hit him unless God allows it. Do you see the problem? If this argument worked, then it would absolve you of all responsibility for anything. To put this another way, even if God exists and nothing happens without him allowing it, what makes you think that he wouldn’t allow us to cause something really harmful, like climate change?

click here for the full version of #24

Bad Argument/Myth #25: Man is not powerful enough to cause climate change

Reality: This is an ad hoc fallacy. It is a baseless assumption that I would never accept unless I was already convinced that climate change wasn’t true. Even if God is real and the Bible is true, there is no Biblical support for this argument, nor is there any reason to think that God drew and arbitrary line at climate change and said, “this far, no further!” If God exists, he has obviously allowed us to do a great many terrible things.

click here for the full version of #25

Related Posts

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25 myths and bad arguments about climate change

Global warming is arguably one of the most controversially topics among the general public. The internet is full of websites that are devoted to arguing against climate change, and politicians routinely claim that it’s a myth. Nevertheless, among the scientific community, there is no serious debate. Yes, there are a few contrarianism (as there are for virtually every topic), but there is an overwhelming agreement that we are causing the climate to change, and that agreement is based on an extraordinary mountain of evidence. Nevertheless, given the amount of junk science on the internet, this disconnect between what people think and what scientists have found is hardly surprising. Therefore, I want to clear up some of the confusion surrounding this topic, and in this post, I will debunk 25 myths, misunderstandings, and faulty arguments about climate change.

At the outset, I want to explain the basics of anthropogenic climate change because there seems to be a lot of confusion over the fundamental concepts. In a nutshell, energy from the sun enters the earth as a spectrum of wavelengths, including both visible light and some higher energy wavelengths (such as ultraviolet [UV] radiation), but some energy is lost and absorbed as the light passes through our atmosphere. The remaining energy is partially absorbed by the earth itself, but much of it is radiated back off of the earth’s surface as lower energy infrared radiation (IR), which is basically just heat energy. Not all of that energy leaves our planet, however, because we have numerous greenhouse gasses in our atmosphere (such as carbon dioxide [CO2]) that do not absorb the higher energy wavelengths (like UV), but do absorb the lower energy IR. Thus, they trap some of that heat energy and prevent it from exiting the planet. This is usually a good thing, because earth would be inhospitably cold if all of that IR escaped. However, if those gases are too dense, then too much heat gets trapped, and the earth warms. Indeed, fluctuations in greenhouse gases concentrations were largely responsible for past climate changes (see #8). This is a problem because our modern society produces a large quantity of greenhouse gasses, and we have greatly increased their concentration in the atmosphere (see #10). Now, let’s think about this rationally for a second. If CO2 traps heat, and more CO2 traps more heat, and we have nearly doubled the CO2, what do we logically expect to happen? The answer is obvious: the climate should warm on average. Indeed, that is exactly what the theory of anthropogenic climate change predicts, and, as I will demonstrate, we have repeatedly verified that prediction.

At this point, I imagine that no one is convinced, and you are probably getting ready to hurl some counterargument. Before you do that, however, please read the arguments below and actually consider the possibility that you might be wrong. I want you to critically evaluate your views and rationally examine the evidence that I have presented. I have backed up every factual claim with citations to the relevant peer-reviewed literature, so you can go to the original sources and make sure that I am not misleading you. Also, for your convenience, I have grouped the arguments into categories and included hyperlinks for them below (you can also just scroll down the page).

Because this post is quite long, I have also written an abridged version in which I deal with each argument in five sentences or less.

Climate change isn’t happening

Climate change isn’t caused by us

Scientists have been wrong before and/or they are incompetent and corrupt

 Miscellaneous

Bad Argument/Myth #1: It snowed, so global warming must not be true
Reality: Weather and climate are not the same thing

stephen-colbert-global-warmingThere are several logical fallacies and problems that are occurring here. First, this is a straw man fallacy/reductio ad absurdum fallacy, because no climate change models have predicted that it will never snow. Winters will, on average, be warmer but that doesn’t mean that it will never snow or even that we won’t have large snow storms. Second, this argument confuses weather with climate. Weather is what occurs over short period of time; whereas, climate is what occurs over long period of time. An individual blizzard is a weather event, and you cannot use that as evidence against climate change.

This brings me to the final major problem with this line of reasoning: using individual weather events to argue that the planet isn’t warming commits a Texas sharpshooter fallacy. That’s basically just a fancy way of saying that you are cherry-picking. You can’t focus on a single weather event, and ignore the overarching warming patterns and changes that are taking place all over the world (see #2, 3, and 22). On that note, it is also invalid to use individual heat waves as evidence for climate change; however, when we look at the big picture and use all of the available evidence, the warming trend is unequivocal, and heat waves are increasing (Luber and McGeehin 2008).

In short, the fact that it was cold for a brief period in the specific part of the world that you live in does not in any way, shape or form suggest that the average temperature of the entire planet is not increasing.

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Bad Argument/Myth #2: The ice in Antarctica is actually increasing
Reality: Antarctic sea ice is increasing, but most ice shelves are melting

It is true that the sea ice in the Southern Ocean (around Antarctica) is increasing, but there are several reasons why that doesn’t discredit climate change. This is another Texas sharpshooter fallacy. You can’t focus on increases in this one ice shelf while completely ignoring the fact that Arctic sea ice has declined substantially (Stroeve et al. 2015), glaciers are rapidly retreating all over the world (WGMS 2013), sea levels are rising (Yi et al. 2015;  NOAA), etc. The planet is warmer now (on average) than it was in the recent past. That is a fact that is not up for debate (for example, see NASA’s excellent visualization of climate change from 1880–2015, as well as bad argument #3). Also, it is important to note that climate change does not predict that every part of the earth will be warmer all of the time. The average temperature is increasing, but that does not mean that every single spot will be warmer.

Finally, it is worth mentioning that the situation with the Antarctic Sea ice is more complicated than simple temperatures. The full explanation far too complex to be dealt with in detail in this post, but in short, the increase is from a combination of factors including ozone levels, changes in wind currents, and changes in ocean currents (some of which are caused by melting Arctic ice; Gillett and Thompson 2002; Zhang 2007).

Note: some people erroneously argue that the Arctic sea ice isn’t decreasing either, but that argument simply cherry-picks a handful of years and ignores the overarching trend, so it is not logically or statistically valid (Swart et al. 2015).

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Bad Argument/Myth #3: Global warming has paused
Reality: No it hasn’t. This claim is based on cherry-picked data and inappropriate statistics

The accumulation of energy over time. You'll notice that most of the energy is getting trapped in the oceans. Image via Rhein et al. 2013.

The accumulation of energy over time. You’ll notice that most of the energy is getting trapped in the oceans. Image via Rhein et al. 2013.

I have explained this one in detail elsewhere, so I will be brief. First, to argue that the climate has paused, you have to cherry-pick your data set. We can measure the temperature lots of different ways and different places (satellites, ocean surface temperatures, deep ocean temperatures, etc.), and there is nothing in the science of climate change that says that all of the different parts of the earth will warm equally or at the same rates (in fact we expect them to respond differently). In contrast, if you are going to say that climate change has paused, you will need to demonstrate a pause across all of the data sets (i.e., for climate change to have paused, the total amount of energy that the earth is trapping needs to have leveled off). When we look at the data, however, only the satellite measurements show a “pause.” The other data sets (such as NASA’s global Land-Surface Air and Sea-Surface Water Temperature Anomalies data set) very clearly show that warming is continuing. The warming is especially pronounced in the oceans, which seem to have absorbed most of the excess heat (Balmaseda et al. 2013; Rhein et al. 2013; Glecker et al. 2016).

This shows the temperature data once the effects of El Ninos, solar fluctuations, and volcanoes. Image via Open Mind.

This shows the temperature data once the effects of El Ninos, solar fluctuations, and volcanoes. In other words, this is the change that is attributable to us. Image via Open Mind.

Additionally, within the cherry-picked data set, you are going to have to cherry-pick the year that you want to start looking for a trend (usually 1997 or 1998). Any year prior to 1997 shows a significant warming trend, and many of the years after 1998 (including 1999) show a significant warming trend. So if you want to say, “the climate hasn’t warmed since 1998,” I can respond with, “the climate has warmed since 1999.” Additionally, those satellite measurements are extremely sensitive to El Niños, and 1998 was an El Niño year. However, if we remove the effects of El Niños, the anthropogenic warming trend clearly emerges (Foster and Rahmstorf 2011).

So to claim that global warming has paused, you have to cherry-pick your data set, cherry-pick your years, and ignore the confounding factors, none of which is logically or scientifically valid. Indeed, the actual scientific analyses of the data show that warming has not paused at all (Easterling and Wehner 2009; Santer et al. 2011; Karl et al. 2015; Lewandowsky et al. 2015a,b).

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Bad Argument/Myth #4: Global warming wasn’t happening so they changed to name to climate change
Reality: Global warming is happening, and the name change is irrelevant

If find this argument rather bizarre, and honestly, to me, it reeks of desperation, but let’s talk about it for a minute anyway. Overtime, scientists have switched from talking about “anthropogenic global warming” to “anthropogenic climate change.” This has erroneously led some people to argue that they switched the names to cover their tracks because the planet wasn’t warming. In reality, scientists have been talking about changes to the entire climate from the outset. In fact, scientists were talking about “climate change” before they knew that the direction of the change would be towards warming. In other words, climate change has always been about far more than just warming because it involves shifts in rainfall patterns, storms, the timing of seasons, etc. So the change in name had nothing to do with a change in the predictions or observations about what was happening.

So why did they change the name? Well, the term “global warming” was leading to all manner of silly arguments (like #1 and 2). People erroneously inferred that “global warming” meant that all parts of the world would be warmer all of the time, which is incorrect. The average temperature is increasing, but that doesn’t mean that it will always be warmer everywhere. Indeed, some areas may even become cooler. Further, the actual change in temperature is only one part of what is happening. So the name change was just an attempt to be more accurate and avoid confusion, but apparently it backfired. Regardless of what you want to call though, the climate is changing, the average temperature is increasing, and we are causing it.

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Bad Argument/Myth #5: The models have all been wrong
Reality: The models have been very accurate

The models have actually done a remarkably good job of predicting climate change (Hansen et al. 2006; Marotzke and Firster 2015). I’m sure that if you dig through the literature, you can find some models somewhere that have been wrong, but the biggest models that most governments and scientists cite have been largely correct. If you want to see this illustrated, Skeptical Science did a nice job of visually comparing the IPCC predictions with the observed warming as well as the failed predictions of climate change deniers. Nevertheless, as a scientist my preference is always the peer-reviewed literature, so in addition to the two papers that I cited at the start of this section, you can also read Frame and Stone (2012) which compared the IPCC’s 1990 prediction with the current warming and found that it was very accurate. Similarly, Rahmstorf et al. (2012) looked at the predictions from the third and fourth IPCC models, and found that the observed trends matched the models. Additionally, the figures that you often see comparing the predictions with the observations often used disparate methodologies, which result in serious biases. Once you correct for that problem, the agreement between the models and the observed warming is much better than what many climate change deniers would have you believe (Cowtan et al. 2015; also, see #22 for evidence that many of the predictions other than increasing temperatures are already coming true).

These are hypothetical data that illustrate the fact that whether or not a model worked should be evaluated based on whether or not the observed data fell within the 95% confidence interval of the model.

These are hypothetical data that illustrate the fact that whether or not a model worked should be evaluated based on whether or not the observed data fell within the 95% confidence interval of the model.

Part of the problem here stems from people either misunderstanding or deliberately misrepresenting how predictive models work. Many people have the unrealistic expectation that the observed data need to be a near perfect match for the prediction line, but that’s not actually how things work. For example, take a look at the hypothetical data above. If I asked you whether or not the model’s predictions came true, you would likely say that they didn’t, but in actuality, they did. You see, when scientists use statistics, whether it is making a prediction, stating a mean, etc., we never expect the true value to exactly match our predictions/estimates. Rather, we report a central value and calculate confidence intervals around that central value. This is the case because there is always variation in the data, and there will always be lots of factors that affect it. So models predict a range of values that are denoted by the confidence intervals. As a result, when you look at a figure like the one above, you should not be seeing whether or not the observed line perfectly matches the predicted line. Rather, you should be seeing whether or not the observed line falls within the 95% confidence intervals for the predicted line. When we apply this to climate change models, we see that in some cases, the observed temperatures are below the central prediction line, but they are still within the 95% confidence intervals, which means that the models were reasonably accurate. This is a really important point. If someone is showing you a comparison between a model and an observation, but they don’t include confidence intervals, you should extremely skeptical, because those confidence intervals are absolutely essential.

Additionally, it is worth noting that many of the models made several predictions based on different levels of greenhouse gas emissions, so you always have to make sure that you are comparing the observed warming with the predicted warming given our rate of emissions. In other words, if you compare the worst case scenario lines with the observed warming, you find a very poor match, but that is because the worst case greenhouse emissions didn’t occur, so that comparison is invalid. Also, realize that these models are affected by natural factors that we can’t predict. For example, our predictions about the effects of greenhouse gasses may be spot on, but if there are more volcanic eruptions than expected, that will affect the overall trend.

Finally, and perhaps most importantly, realize that the models are simply predicting future climate change. The fact that we are currently causing the climate to change is not in any way based on the models in question (see #7, 8, 9, 10, and 11). So even if all the models were wrong, that would not in any way shape or form discredit the fact that we are changing our planet’s climate. Rather, it would simply mean that we don’t have a good idea of how those changes will affect the future.

Note: You may have seen a very popular graph by Roy Spencer and John Christy that claims to show a large disagreement between the predictions and observations, but that graph has numerous problems such as cherry-picking data sets and start points, and it is not at all statistically valid (details here, here, and here).

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Bad Argument/Myth #6: Polar bear numbers are actually increasing!
Reality: No, they aren’t. They are decreasing

First, this isn’t actually an argument about climate change. Even if it was true (which it’s not), it would not mean that climate change wasn’t happening. Nevertheless, many people seem to view it that way, and polar bears certainly are the poster child of global warming activism, so let’s briefly talk about this.

This claim is actually a complete and total myth. It is perpetuated by citing dodgy an inaccurate estimates of past polar bear numbers and cherry-picking examples. Sure, if you did aground, you can find certain situations in which a particular group of polar bears is doing well (often from increased hunting restrictions), but when you look at the big picture, and the comprehensive reviews that look at the polar bear population as a whole (rather than cherry-picking populations) there is a very clear downward trend (Schliebe et al. 2006; Stirling and Derocher 2012). Additionally, the full impact of the vanishing ice shelves becomes clear when you model future polar bear declines given the current loss of ice. This, once again, predicts “drastic declines in polar bear populations” (Hunter et al. 2010; Molnar et al. 2011).

At this point someone will probably make a ridiculous comment like, “but polar bears can swim.” Yes, they can, but that is not the point. The ice shelves are where they hunt, raise their young, etc., and that habitat is disappearing. They can’t just swim to better habitat. That isn’t how this works, and the effects of this reduction in ice are extremely clear. It’s not just that the populations are declining, rather, cub mass is going down, juvenile recruitment is going down, body condition is going down, etc. (Rode et al. 2010, 2012). All of these factors are because the habitat that they need is vanishing, which means that they can’t find enough food, can’t raise proper-sized young, and will ultimately disappear from much of their current range if climate change isn’t halted.

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Bad Argument/Myth #7: The climate has changed in the past, so the current warming is natural. It’s the sun, volcanoes, Milankovitch cycles, etc.
Reality: We have tested the natural factors, and they cannot explain the warming

This is probably the single most common argument against climate change, and it is often accompanied by ridiculous questions like, “who was producing CO2 in the past? Dinosaurs?” However, despite its common use, this argument is extremely flawed. I explained this one in detail here, but in short, the fact that climate changed naturally in the past only tells us that it is possible for the climate to change naturally. It does not indicate or even suggest that the current warming is natural (i.e., this is a non-sequitur fallacy). You have to provide actual evidence that the current warming is natural.

Additionally, we have carefully examined the sun, volcanoes, Milankovitch cycles, etc. and none of them can explain the current warming trend (Meehl, et al. 2004; Wild et al. 2007; Lockwood and Frohlich 2007, 2008; Lean and Rind 2008; Foster and Rahmstorf 2011; Imbers et al. 2014). Indeed, numerous studies have used statistical models to examine the possibility that the current warming is natural, and they have consistently found that natural factors alone cannot explain the current warming. When you add anthropogenic greenhouse gasses into the statistical models, however, you get a tight match between the observed and expected values (Stott et al. 2001; Meehl et al. 2004; Allen et al. 2006; Lean and Rind 2008; Imbers et al. 2014). To put it simply, we have tested the natural factors and we have tested the anthropogenic factors, and the anthropogenic factors are necessary to explain the warming trend. This is extremely clear evidence that we are the cause. Additionally, several studies have found that CO2 was actually the major driver of past climate change (Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012), so it should hardly be surprising that we can cause the climate to change by producing CO2. Finally, as I will explain in #9, we have directly, empirically tested the notion that our CO2 is causing the planet to trap more heat, and (spoiler alert) it is (see #8, 9, 10, and 11 for more about CO2).

I short, we know that the current warming is not natural because we have tested that hypothesis and it failed. That is how science works. When a hypothesis fails, you reject it and move on.

This figure from Hansen et al. 2005 shows the effect of both the natural and anthropogenic drivers of climate change. Notice how only anthropogenic sources show a large warming trend. Also, see figure 2 of Meehl et al. 2004.

This figure from Hansen et al. 2005 shows the effect of both the natural and anthropogenic drivers of climate change. Notice how only anthropogenic sources show a large warming trend. Also, see figure 2 of Meehl et al. 2004.


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Bad Argument/Myth #8: During past climate changes, the CO2 follows the temperature increase
Reality: Temperature leads CO2 at first, but CO2 soon overtakes it and drives most of the warming

This argument claims that when we look at past climate changes, we see that the temperature changes, then the CO2 changes. This is true at first, but it is only part of the story. There are numerous feedback mechanisms involved in climate change. In other words, one event can trigger another event, which triggers another event, etc. In this case, what happened in the past was that a small amount of warming (usually regional) from factors other than CO2 (such as Milankovitch cycles) caused the oceans to warm up and release the CO2 stored in them (Martin et al. 2005; Toggweiler et al. 2006; Schmittner and Galbraith 2008; Skinner et al. 2010). Then, that increase in CO2 caused the majority of the warming (Shakun et al. 2012). So CO2 was actually the major driver of past climate changes (Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012).

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Bad Argument/Myth #9: CO2 only makes a small portion of the atmosphere
Reality: CO2 is not abundant, but that does not make it unimportant

This argument claims that CO2 only makes up roughly 0.04% of the earth’s atmosphere, which is such a small amount that it cannot be important for climate change. The premise that CO2 is relatively uncommon is true, but that does not make it unimportant. It is extremely well established that CO2 traps heat (you can find a list of papers demonstrating this at AGW Observer), and it is a scientific fact that CO2 is extremely important for regulating the temperature of our planet. Indeed, that tiny percentage of CO2 is the difference between our nice warm world and an inhospitably cold world, and we know that past climate changes have been largely driven by CO2 levels (Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012; see #8). Additionally, for any situation like this, you need to look at the actual amount of change, and the fact is that we are rapidly approaching a doubling of CO2 in the atmosphere (see #10). So since we know that CO2 is important even though it is not abundant, it should not be surprising that doubling that important gas will have huge consequences.

Finally, the CO2 hypothesis makes a nice, testable prediction. Remember, the theory of anthropogenic climate change postulates that our CO2 is trapping outgoing IR (heat), thus warming the planet (see introduction). If that claim is true, then we should see that the amount of IR leaving the planet has decreased over time, and that decrease should match the increase in CO2. That is, of course, exactly what satellite data show (Harries et al. 2001; Griggs and Harries 2007). The IR leaving the earth since the 70s has decreased, and that decrease matches the increase in CO2. This is a direct test of anthropogenic climate change and cannot be explained by anything other than our CO2 trapping heat. If you want to argue to the contrary, then please explain to me where the IR is going?

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Bad Argument/Myth #10: We only emit a tiny portion of the earth’s CO2
Reality: Before us, the CO2 cycle was in balance, now more is produced than is removed

It is true that natural sources of CO2 produce far more of it per year than we do. Indeed, we produce around 29 gigatonnes annually, whereas nature produces around 771 (IPCC AR4). However, prior to us, the system was in balance, with roughly the same amount being removed and produced (plants, the ocean, etc. all remove some CO2). Thanks to humans, however, that balance has shifted and now more CO2 is being produced than is being removed. Think of it this way: if I give you $1,000 and at the same time, you give me $1,000, we can keep doing that forever, and neither of us will gain money. Now, suppose that we both continue doing that, but during every transaction someone else gives you $30, and you don’t give that money back. Thirty dollars is tiny compared the $1000 that was already being exchanged, but because that extra $30 isn’t removed, suddenly, you are going to be gaining money, and after a few dozen transactions, your money will have doubled. That is exactly what is happening with CO2. More is being produced than removed, therefore it is increasing. Indeed, CO2 levels have increased rapidly since the start of the industrial revolution, and they are currently at their highest point in past 14–16 million years (Tripati et al. 2009).

Carbon dioxide isotope ratios CO2

These data come from Wei et al. 2009, but the legend of this figure was modified for readability by skepticalscience.com (the data themselves were in no way manipulated as you can see in Figure 4 of Wei et al.)

Finally, we know that the CO2 is from us because of the isotope ratios. I explained this in detail here, but briefly, carbon has two stable isotopes: carbon-12 and carbon-13, but the ratio of carbon-13/carbon-12 in the atmosphere was historically different than the ratio in our fossil fuels. Thus, if burning fossil fuels is putting CO2 into the atmosphere, we would expect the ratio of carbon-13/carbon-12 in the atmosphere to shift to be closer to the levels in the fossil fuels, and that is exactly what studies have found. This is extremely clear evidence that the CO2 is from us (Bohm et al. 2002; Ghosh and Brand 2003;Wei et al. 2009).

On a side note, it is also worth mentioning that volcanoes produce less than 1% of the Co2 that we do annually.

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Bad Argument/Myth #11: Water vapor is a far more potent greenhouse gas than CO2
Reality: Water vapor increases in response to temperature and is a feedback mechanism that increases the effects of CO2

It always amazes me that people assume that scientists missed something as blatantly obvious as the fact the water vapor traps far more heat than CO2. In reality, scientists are well aware of this fact and have incorporated it into their calculations, but there is something very important that you need to realize about water vapor. Namely, it increases or decreases in response to temperature. The amount of water vapor in the atmosphere is dependent on the temperature of the environment, as a result, to increase the water vapor in the atmosphere, you must first increase the temperature. So, water vapor is a feedback mechanism, wherein CO2 from us causes some warming, that warming increases the amount of water vapor in the atmosphere, and that water vapor causes even more warming (Held and Soden 2000; Philipona et al. 2005). So ultimately, the warming is still from us producing CO2.

If you don’t believe me, you can easily do an experiment yourself. Take several glasses with equal amounts of water and place some in a cool shaded area while you place others in the hot sun, then see which ones evaporate first (i.e., which one is converted into water vapor). Or, to put this another way, if you place a glass of water in a stable environment, it will not spontaneously start evaporating more rapidly. If you want to increase the rate of evaporation then you need to increase the temperature, decrease the humidity, etc. So, in short, the recent increase in water vapor levels is a result of the global warming that we are causing, and scientists are fully aware of this and include that information in their models (Held and Soden 2000).

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Bad Argument/Myth #12: In the 70’s scientists predicted an ice age
Reality: Very few scientists predicted global cooling

There was never a scientific consensus on global cooling. It is true that there were a handful of papers on the topic (7 to be exact), but during that exact same time, 42 papers were published on global warming (Peterson et al. 2008). That’s right, there were six times as many papers on global warming as there were on global cooling. So there was never a large consensus that we were causing cooling, and even in the 70’s many scientists were saying that we were causing global warming (to be clear though, it was not well established yet).

Inevitably, someone is going to read that and say, “You don’t know what you are talking about, I lived through the 70s, and I remember all of the ice age predictions.” However, while you may have lived through that time period, I am willing to bet that you weren’t reading the scientific literature. I’m not denying that the media went nuts with the idea of an ice age, but that is not at all the same as saying that scientists were predicting an ice age. The media (and even popular science magazines like National Geographic) love to sensationalize things, and they continuously get scientific facts wrong. So it really shouldn’t surprise anyone to learn that the media was making a mountain out of a mole hill.

Finally, it is worth pointing out that even if scientists had predicted global cooling in the 70’s, that wouldn’t mean that they are wrong now (science has come a long way in the past four decades). Also, the first prediction that our emissions would lead to global warming dates all the way back to 1896. Climate change is not a new hypothesis that scientists invented in the 80’s.

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Bad Argument/Myth #13: It’s just a theory, not a fact
Reality: In science, a theory is a well-established concept that explains facts, not an educated guess

For this argument, climate change deniers take a page straight from the creationist playbook, but the argument doesn’t work any better here than it does for creationism. You see, the term “theory” has a very different meaning in science than it does in the general public. Let me explain.

Many people think that a “fact” is something that has been proven and scientists are totally sure of, whereas a “theory” is something that is more speculative and has not been properly confirmed. That dichotomy is, however, completely incorrect. Nothing in science is ever “proved.” Rather, things are simply supported by the current evidence. So the difference between a theory and a fact has nothing to do with our certainty. Rather, a fact is a single observation, result, etc. whereas a theory is a broad and rigorously-tested explanatory framework that both explains the facts and allows us to make predictions about what future experiments should show.

For example, if I drop a pen, then I have just demonstrated the fact of gravity (i.e., it is a fact that gravity caused my pen to drop), but what does that really mean? That’s not really an explanation. The explanation comes from the theory of universal gravity, which states that all bodies that have mass produce gravity and are acted upon by the gravity of other bodies. That theory explains the fact and allows us to predict the outcomes of future experiments (e.g., if I drop another pen, it will fall). Evolution is the same thing. It is a scientific fact that life on earth has evolved over millions of years, and the theory of evolution by natural selection explains how and why that evolution occurred as well as allowing us to make predictions about the results of future experiments. Similarly, if you get the flu, it is a fact that a virus made you sick, but it is the germ theory of disease that provides the general explanation that diseases are caused by viruses, bacteria, etc. I could go on, but hopefully you get the point. You should notice in all of these cases that we are extremely confident that the theory is true. So they aren’t theories because they are speculative or haven’t been confirmed. Rather, they are theories because they are explanatory frameworks rather than isolated results.

The exact same thing is true for climate change. It is a scientific fact that climate change is occurring, and the theory of anthropogenic climate change is the explanation for why that change is occurring. Just as with the other theories, this theory is supported by thousands of scientific studies. It is, by any reasonable standard, “settled science.” Sure, there are still aspects of it that we don’t understand (just as there are aspects of evolution that we don’t understand), and sure, there are a few dissenting voices (just as there are for the other theories), but we are incredibly certain that climate change is occurring. We have tested the predictions of the theory of anthropogenic climate change over and over again and they have consistently come true (see #5, 7, 9, and 22).

Note: Sometimes gravity is referred to as a “law” rather than a “theory.” The distinction between the two is not well agreed upon at all, and many people use them interchangeably. Other people suggest that we should use “law” to refer to the mathematical component and “theory” to refer to the explanatory component. However, regardless of the technical definition that you want to apply, they are both generally considered to have the same level of certainty.

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Bad Argument/Myth #14: But scientists have been wrong in the past, and we can’t be totally certain that climate change is true
Reality: It is always possible that the current scientific consensus is wrong, but you have to present actual evidence that it is, otherwise you are making a baseless assumption

I explained the problems with this argument in detail here, but in short, the fact that scientists have been wrong before does not mean that they are wrong now. You have to present actual evidence that they are wrong now, otherwise this is what is known as an argument from ignorance fallacy. Indeed, if this argument worked, you could use it anytime that you wanted. For example, you could say, “scientists say that gravity is true, but scientists have been wrong before so I don’t have to accept them now” or “scientists say that we are breathing oxygen, but scientists have been wrong before so I don’t have to accept them now.”

Second, realize that it is not about the scientists themselves, rather it is about the scientific evidence, and the scientific evidence for climate change is extremely robust. It is supported by literally thousands of studies. There really aren’t any topics that have been this thoroughly studied where it turned out that the scientific evidence was totally wrong. Remember, science as the formal system of study that we know today has only existed for the past 150 years or so, and there have certainly been many great advances during that time, but few (if any) ideas with this level of support have been totally overthrown. It is also worth mentioning that because science is a fairly recent discipline, you cannot compare scientists today to the “scientists” who thought the earth was flat, alchemy worked, etc. Those people were not doing “science” by today’s standards.

Finally, regarding the claim that we can’t be totally certain of climate change, that claim is true for every single avenue of science. It is always possible that there is an answer that we missed. Indeed, it is possible that we are all in the Matrix, and none of this is even happening. That is why science never “proves” anything. Rather, it shows us what is most likely true based on the current evidence. It is, for example, possible that we are wrong about gravity, but it would obviously be absurd to say, “we can’t be totally certain about gravity, therefore I reject it.” Indeed, this is another argument from ignorance fallacy. The fact that there might be something else does not make it logical to actually think that there is something else. This is especially true in this case because we have tested all of the known drivers of climate change, and none of them can explain the current warming (#7). Further, we know that CO2 is largely responsible both for our climate and for past climate changes (#8), we know that we have greatly increased the CO2 in the atmosphere (#10), we know that the earth is trapping more heat (#9), and we know that including our CO2 in the calculations explains the current warming trend (#7). So if you want to claim that something else is happening, you need to provide actual peer-reviewed evidence for the existence of this mysterious factor. That is how the burden of proof works.

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Bad Argument/Myth #15: There are thousands of scientists who disagree (e.g., the Oregon Petition)
Reality: Science is not a democracy, most climatologists agree that we are causing climate change, and the Oregon Petition is a fraud

scientific consensus on global climate change, global warming

Image via James L. Powell. More details on the scientific consensus here.

First, it is important to realize that this argument is nothing more than an appeal to authority fallacy. It is always possible to find people with advanced degrees who agree with you, but that does not make your position any more legitimate. You have to look at the evidence, not the people who support it. Further, if you want to focus on the scientists themselves, you are going to run into huge problems because an overwhelming majority (well over 90%) of climatologists agree that we are causing the planet to warm (details here), and many of the prominent climatologists who disagree are funded by oil companies (for example, Dr. Willie Soon). To be clear, that doesn’t make them automatically wrong, but it does mean that we should scrutinize them closely.

Nevertheless, many people try to assert that in actuality, thousands of scientists deny anthropogenic climate change, and they usually do this by citing the “Oregon Petition” which (depending on what source you look at) received the signatures from 16,000, 30,000, 31,000 or 32,000 US scientists affirming that they do not agree that humans are causing climate change. There are several important things to note here. First, science is not a democracy. You don’t vote on whether or not something is a fact. That’s just not how this works. Second, 32,000 people with a B.Sc. or higher (which was the criteria for this petition) is actually a tiny percentage. Skeptical Science estimates that it is around 0.3% of “scientists” (by the standards of the petition). So that is hardly compelling.

More importantly, however, most of the signatories weren’t climatologists, and many weren’t even real scientists. Having a B.Sc. does not make you a scientist. Further, even among those with advanced degrees, there were signatures from veterinarians, architects, orthopedic surgeons, etc. That is important, because it really doesn’t matter whether or not a surgeon thinks that climate change is real. That is ridiculously far outside of their field.

When it is all said and done, it appears that only 39 of the signatories were actually climatologists. Thirty-nine is hardly an impressive number. There were also many other problems with the petition that you can read about here and here.

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Bad Argument/Myth #16: “Climategate” showed that scientists are falsifying data
Reality: The stolen emails did not show any evidence of corruption

This myth has largely died out, but I still encounter some who use it, so let’s talk about it for a minute. Several years ago, over 1,000 private emails and documents from leading climatologists were hacked and released to the public, and many climate change deniers claimed that those emails showed evidence that scientists were falsifying data and it was all a conspiracy. Let’s think about that for a second. If that was actually the case, we would expect most of those emails to be about orchestrating a conspiracy, and very few (if any) should be about doing real analyses on real data. In contrast, what the emails showed were tons of conversations about real data, and a handful of emails that were twisted and taken out of context to try to create the illusion of unethical behaviour.

The most commonly cited email is one from Phil Jones, which states:

“I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (i.e. from 1981 onwards) and from 1961 for Keith’s to hide the decline.”

Out of context, that sounds really bad, but in context, it is obvious that “trick” was being used to mean “a clever way to do something” not “a deceptive way to do something.” Indeed, we use the word “trick” that way all of the time, and it is not uncommon to hear people say things like, “the trick to doing this is…” Second, the “decline” there is not referring to temperatures. Rather, it is referring to a decline in the quality of tree ring data. So when you understand the analyses that they were working on, and look at the quote in context, it is obvious that nothing unethical was occurring.

Another common quote is from Kevin Trenberth:

“The fact is that we can’t account for the lack of warming at the moment and it is a travesty that we can’t.”

Again, out of context, that sounds pretty damning, but in context, he was not talking about the warming of the entire planet, but rather the flow of energy through the earth, and the fact that there are parts of that system that we do not yet understand. Here is the paper that Trenberth was discussing (Trenberth 2009) and you can find more details at Skeptical Science.

There were a handful of other emails that people pounced on, but they had the same problems (i.e., they were taken out of context). Indeed, the situation has been examined by multiple different independent investigations (including the National Science Foundation, US Environmental Protection Agency, UK House of Commons Science and Technology Committee, Pennsylvania State University, and University of East Anglia), and all of them concluded that there was no evidence that the scientists were manipulating data, involved in a conspiracy, etc. So like the vast majority of conspiracy theories, this is a whole lot of nothing. It sounds bad at first, but when you actually look at all the details, there is nothing there.

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Bad Argument/Myth #17: Scientists are manipulating the data to make it look like warming!
Reality: Scientists are correcting for biases, not “manipulating” the data

This argument has been popularized by Paul Homewood on the blog notalotofpeopeknowthat.wordpress.com, where he argues that scientists are manipulating the data to make it show an unrealistic warming trend (specifically data in the Global History Climatology Network database (GHCN)). Other authors, like Christopher Booker, have run with this idea publishing articles with eye-catching titles like “The fiddling with temperature data is the biggest scientific scandal ever.” It is true that scientists have made adjustments to the data, but it is completely false that there is anything dishonest or deceptive about those adjustments. They are “corrections” rather than “manipulations.”

I explained this in more detail here, but in short, essentially all real data sets have biases and errors that have to be corrected. Scientists almost never collect perfect data sets that are ready to be analysed as soon as they are gathered. Rather, they almost always have to be corrected for errors and biases. That is just a fact of real data. In the case of climate data, the data have been collected over many years using different methods, and different methods have different biases. Therefore, the only way to use all of those data sets is to adjust for those biases in methodologies.

Let me illustrate. Imagine that 40 years ago, you set up a thermometer in your back yard, and used it to record the temperature every day at 9:00AM, but after several years, you switched to 10:00 AM. If you want to look at long term trends, you are going to have to adjust your data to account for that change in methodologies, otherwise you’ll get an unrealistic warming trend. The same type of thing happens with real data sets. Ocean buoys drift, methods change, the environment around weather stations changes, etc. All of those factors have to be accounted for to properly analyse the data. So rather than being a “scandal” these adjustments are the proper way to treat the data. Further, climate change deniers act as if this is some secret that scientist have been hiding, but the reality is that they have been extremely open about these adjustments, and have publically documented all of their methods from day 1. So this argument is baseless.

Note: you can find more information about how and why the GHCN data are adjusted in their technical report (Williams et al. 2012), and you can find a very detailed and useful general explanation about adjustments to temperature data sets by Scott Johnson at arstechnica, “Thorough, not thoroughly fabricated: The truth about global temperature data.”

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Bad Argument/Myth #18: It’s a liberal conspiracy/It’s all about the money!
Reality: There is no basis for this claim. It is an assumption, not a legitimate argument

First, for both of these claims, in order for them to be legitimate, you must provide actual evidence to support them. Otherwise, the claims are invalid and commit ad hoc fallacies. In other words, you cannot just assume that essentially all scientists are corrupt. You have to provide actual evidence that they are corrupt. Further, it is utterly absurd to think that the vast majority of the world’s climatologists, virtually every government agency in the world, essentially all of the worlds most respected scientific organizations, etc. are involved in a massive conspiracy. Do you really think that nearly all of the world’s governments are collaborating together?

Also, it is important to ask why they would conspire to make the entire world think that climate change is happening. You could argue that scientists are in it for grant money, but that argument misunderstands how grants work, and it doesn’t really solve the problem, because you need to explain why governments would give out grant money for climate change research. I have yet to have anyone explain to me how governments would benefit from creating such a huge hoax.

global warming money

Details and sources here

With regards specifically to the claim that it is all about money, I explained the problems with that in detail here, but to be brief, first, there is once again no clear financial motive. There is no obvious reason why governments would make this conspiracy. Additionally, if we are going to go down this road, then let’s flip things upside down and ask the opposite question: who would benefit from opposing climate change research? The answer to that question is pretty obvious: oil companies. Indeed, it is well known that oil companies have been extremely active in funding denialist organizations and politicians as well as funding the handful of climatologists who don’t think that we are causing climate change. At this point, we have run into one of the biggest problems with this argument. Namely, if scientists could really be bought off so easily, then why haven’t multi-billion-dollar oil companies been able to buy off more than a handful of them? Given the vast wealth of oil companies, the millions of dollars that they have poured into denialist organizations, and the economically unstable state of most governments, surely oil companies could offer scientists more than governments could. So if scientists are really just in it for the money, why aren’t they all denying climate change?

Finally, throughout this post, I have provided actual evidence that climate change is happening and we are causing it (see #2, 3, 5, 7, 10, and 22). Also, I have debunked several specific conspiracy arguments such as Climategate (#16) and the claim that scientists are manipulating the data (#17).

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Bad Argument/Myth #19: But politicians and the media…
Reality: They are irrelevant

Let me stop you right there, because, quite frankly, I don’t give a crap what politicians and the media think or say. Both of them are repeatedly wrong about the science (on both “sides” of the topic). So I don’t care what Al Gore said or thinks, I don’t care what erroneous claims CNN has made, etc. I care about the science, and the scientific evidence overwhelmingly shows that climate change is happening and it is our fault. Can people spin that for personal gain? Sure, but that doesn’t make the science any less true. Using politicians and the media to attack science is a guilt by association fallacy, because what they think, say, and do is completely, 100% irrelevant to whether or not the science is correct. I care about what peer-reviewed studies have found, not what politicians and news anchors say.

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Bad Argument/Myth #20: Climate change is being caused by the ozone hole (or vice versa)
Reality: The ozone hole and climate change are two separate phenomena that sometimes interact.

There seems to be a lot of confusion about the ozone hole. Many people seem to think that either the hole is causing climate change or climate change is causing the hole. I have heard other people argue that the ozone hole illustrates a great failure of science, because scientists predicted that it would be a problem, but nothing happened. In reality, the ozone hole and climate change are separate phenomena, and “nothing happened” because people actually listened to scientists and changed their behaviors.

I explained the basics of climate change in the introduction, so I’ll just focus on the ozone hole here. Ozone is simply three oxygen atoms bonded together, and it forms from a high energy source (such as UV radiation or electricity) hitting oxygen gas (the O2 we breathe). Thus, it naturally forms in the atmosphere from UV hitting oxygen, and it forms a layer commonly called the ozone layer. This is a good thing because ozone absorbs UV radiation, preventing it from reaching the earth (as opposed to CO2 which does not absorb UV radiation, but does absorb IR). Thus, the ozone layer shields the earth from a large amount of UV that would otherwise reach the surface and cause skin cancer and numerous other problems.

The concept of an ozone hole became an issue when scientists realized that certain chemicals that we were using (such as chlorofluorocarbons, halons, and other chemicals with chlorine or bromine) were entering the atmosphere, chemically interacting with the ozone layer, and actually depleting the ozone. As a result of this depletion, the ozone layer was thinning, with a particularly thin area forming seasonally over the Antarctic. This large thin spot is what is generally referred to as the “ozone hole.”

Scientists realized that this was a problem, and (for once) many governments listened to them and banned the harmful chemicals. As a result, the ozone layer stopped thinning. So the ozone hole does not illustrate a time that science failed. Rather, the science was spot on, and things never became worse because people actually listened to scientists. Indeed, the ozone layer does appear to be recovering (Lefevre et al. 2013), but there is some debate about the extent of the recovery, and the rate of recovery is certainly quite slow (partially because many of the harmful gases persist in the atmosphere, and some countries are still using them).

In short, the ozone hole was caused by our chlorofluorocarbons and similar chemicals depleting the ozone layer, and it was bad because the ozone layer prevents some UV from entering the earth. In contrast, climate change is caused by us releasing greenhouse gasses (an entirely different set of gases), which results in less IR leaving the earth. Thus, they are two separate phenomena, but they do sometimes interact. It is, however, incorrect to say that one causes the other.

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Bad Argument/Myth #21: But CO2 is actually good for plants
Reality: CO2 is good for plants, but the other effects of climate change won’t be

It is true that increased CO2 levels will generally result in more plant growth, but that relationship is complicated (Robinson et al. 2012). Indeed, there are lots of other factors to consider, such as changing precipitation patterns, which are often very harmful to plants (Allen et al. 2010; Carnicer et al. 2011). So many plants will actually be negatively impacted. Finally, this argument is really quite irrelevant, because even if plants would universally benefit from increased CO2, that wouldn’t mitigate the sea level rise, increased heat waves, etc. (see #22). In other words, the CO2 levels are increasing, so the plants clearly can’t keep up (i.e., plant growth isn’t increasing fast enough to balance out the CO2 that we are producing; see #10).

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Bad Argument/Myth #22: It’s not really a big problem because the planet will only warm by a few degrees
Reality: Even 1°C degree would be extremely damaging

There are those who argue that climate change is happening, but it won’t really be serious because the predictions show that it will “only” warm by 1–3°C (the real number will probably be in the middle). First, for my American readers, I should point out that the prediction is in Celsius, so that is 1.8–5.4°F, and that is actually a big deal. Imagine, for example, a hot week of summer with temperatures at 98°F. Now imagine that those days just jumped up to 100°F. That makes a noticeable difference. Similarly, imagine precipitation during a 31°F winter day. That will cause snow, but now imagine that those days are 33°F. See the difference? A few degrees’ matter, and I have just been describing the lowest end of the predictions. In all likelihood, the changes will be in the range of 3–4°F, and that is enough to make a huge difference.

Indeed, we are already seeing the changes. Glaciers and ice caps are melting (WGMS 2013; Stroeve et al. 2015), the sea is rising (Yi et al. 2015), animals and plants are shifting their ranges and behaviours (Root et al. 2003; Tingley et al. 2012), forests are being affected (Allen et al. 2010; Carnicer et al. 2011),  heat waves and heat associated mortalities are increasing (Patz et al. 2005; Luber and McGeehin 2008; Kuglitsch et al. 2010), extreme weather events are increasing (Coumou and Rahmstorf 2012), coral reefs are bleaching (Hoegh-Guldberg and Bruno 2010), droughts are increasing (Dai 2013), etc., and all of these things will only get worse with time. In many parts of the world, it will be harder to grow crops (Schlendker and Roberts 2009), coastal properties will be lost, diseases will expand their ranges, etc. To be clear, this isn’t going to end life as we know it (see #23), but it is an extremely serious problem that will cause a large loss of life, property, and resources, and we need to treat it as the impending threat that it is.

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Bad Argument/Myth #23: It will make humans go extinct/it will be the end of the world
Reality: It will be very damaging, but it won’t end life on planet earth

Following #22, it is important to clarify that although climate change is a very serious problem that will make life on earth more difficult, it is not going to end life as we know it. Indeed, people (especially the media) tend to exaggerate and blow it out of proportion. For example, I once heard a news reporter suggest that food would become so scarce that we would have to resort to cannibalism. Claims like that are just nuts, and they are not supported by the science. I think that this is important to state for two reasons. First, well-intentioned people who accept the science often make unmerited claims, and, second, I often meet people who write climate change off as absurd because they think that the scientists themselves are proposing that climate change will destroy the world. In reality, there are very, very few professional climatologists who make such dire predictions. Again, that is not to say that climate change isn’t a serious problem. It absolutely is a serious problem (see #22), but it’s not going to make me kill you in your sleep and eat your flesh, nor will it cause the human species to become extinct.

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Bad Argument/Myth #24: God is in control
Reality: If you actually think that this claim absolves you from responsibility, then you should never try to prevent anything bad from happening

I am always loath to bring religion into what should be an entirely scientific discussion, because the scientific evidence should be the only thing that matters here. Nevertheless, I often find that Christians try to cop-out of any responsibility for climate change by simply asserting that “God knows best” or “climate change won’t happen unless God allows it to, so there is no point in us worrying about it.” Given that a large portion of the world’s population is Christian and the fact that many Christians won’t accept science if they think it conflicts with their religion, I want to deal with this argument here.

For the sake of debate, let’s assume for a second that the premises of this argument are actually true (i.e., there is an omnipotent, supernatural being who knows everything and is in some way interacting with things on planet earth). If those premises are true, does the conclusion that we don’t need to try to stop climate change follow from those premises? NO! If this argument worked, then Christians should never take action on anything. If, for example, you see that a child is about to get hit by a bus, there is no point in trying to save him because the bus won’t hit him unless God allows it. Similarly, if you see that two countries are about to go to war, you shouldn’t do anything because “God is in control.” Do you see the point? If this argument worked, then it would absolve you of all responsibility for anything. To put this another way, even if God exists and nothing happens without him allowing it, what makes you think that he wouldn’t allow us to cause climate change? We’ve done tons of terrible things to ourselves and this planet, so why do you draw an arbitrary line at climate change? This is a huge assumption and ad hoc fallacy, nothing more (i.e., I would never accept this argument unless I was already convinced that climate change wasn’t true).

Finally, just to prove that I am not committing a false equivalency fallacy, let me set up two analogous arguments.

Argument 1:
1). Science shows that smoking causes cancer
2). God is in control and I won’t get cancer unless he allows it
3). Therefore, I can smoke and not worry about it

Argument 2:
1). Science shows that burning fossil fuels causes climate change
2). God is in control and we won’t cause climate change unless he allows it
3). Therefore, we can burn fossil fuels and not worry about it

Those two arguments are identical, which means that you must either reject them both or accept them both, but I don’t know any Christians who would agree with argument 1 (and just in case you do, we could make it even more extreme by replacing the first premise of argument 1 with something like “Science shows that jumping off a 400-foot cliff causes death”).

Note: To any Christians reading this, before you get all bent out of shape and accuse me of attacking God/the Bible/Christianity, please realize that I am not attacking any of those things or mocking you. I am simply pointing out the logical flaws in one specific argument that many Christians make. I am not making any statements about Christianity as a whole, because this is a post about science, not religion.

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Bad Argument/Myth #25: Man is not powerful enough to cause climate change
Reality: This is a baseless ad hoc assumption, not a logical claim

Following the previous argument, many Christians respond by saying, “but man is not powerful enough to affect the climate.” That response is, however, an ad hoc fallacy. Again, let’s assume for the sake of argument that the premises are true (i.e., God is real, the Bible is true, etc.). There is nothing anywhere in the Bible (at least to my knowledge) that says that man can’t change the climate. If God exists, then he has obviously given us tremendous freedom. Look at the things that we have done and accomplished (some good, some bad). We have eliminated diseases that used to kill thousands of people annually, we’ve removed mountains, we’ve created lakes, we’ve cleared forests, we can take an organ out of one person and put it into someone else, we can restart a heart that has stopped beating, we’ve built planes that can travel around the world, we’ve gone to the moon, we’ve created weapons that can annihilate entire cities in mere seconds, we’ve started wars that have engulfed the entire planet, etc. Given all of that, it is completely arbitrary to draw the line at climate change, which is why this is a perfect example of an ad hoc fallacy. I would never accept that such an arbitrary line exists unless I was already convinced that we aren’t causing climate change. In other words, this argument is a baseless assumption.

At this point, some Christians try to cite Bible verses that say that there will always be a harvest and a winter. That is, however, a straw man fallacy, because no one is saying that winter will cease to exist or that there will be a world-wide famine. Winters will be warmer, and climate change will strain the food supply, but it’s not going to end the world (see #22 and 23).

Finally, I think that everyone can agree that if every country fired their entire nuclear arsenal, we would create a nuclear winter and alter the earth’s climate, which proves that man does have the ability to change the climate, and no, it doesn’t matter that my example used nuclear weapons. Christians’ argument makes an absolute statement: “man cannot change the climate,” and it only takes one counter-example to disprove an absolute (i.e., my example shows that man can change the climate). Nevertheless, many Christians often try to worm out of this by changing the argument to, “man isn’t powerful enough to change the climate via greenhouse gasses,” but by making the argument more specific, you only make the ad hoc fallacy more absurd.

Note: To any Christians reading this, before you get all bent out of shape and accuse me of attacking God/the Bible/Christianity, please realize that I am not attacking any of those things or mocking you. I am simply pointing out the logical flaws in one specific argument that many Christians make. I am not making any statements about Christianity as a whole, because this is a post about science, not religion.
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Conclusion
Let’s review what we have learned, shall we? Many people seem to be under the impression that scientists are hopelessly incompetent and have never bothered to look for obvious natural causes of climate change, but that notion is completely incorrect. In reality, scientists have carefully examined natural sources of climate change, and none of them can explain our current warming (#7). Indeed, the greenhouse gasses that we produce are the only thing that can explain the warming that we are currently seeing (#7 and 9), and yes, we have confirmed that the increase in CO2 is actually from us (#10). It is true that we produce only a small amount of the earth’s CO2, and it is true that CO2 is not abundant to begin with (#9); however, uncommon though it may be, we know that it is incredibly important for regulating our climate and has been largely responsible for past climate changes (#8), and we know that prior to the industrial revolution, the CO2 cycle was in balance with roughly the same amounts of carbon being released and removed (#10). We have now shifted that equation so that more is being released than is being removed, and the result is that the planet is warming. No, the fact that it snowed where you are does not discredit this notion (#1), nor does the fact that Antarctic sea ice is increasing (#2). Those are cherry-picked data points that ignore the overarching pattern of warming and climate changes (#22). Additionally, it is a myth that all of the models have been wrong (most have been quite accurate; #5), and it is a myth that in the 70’s there was a scientific consensus that we were entering an ice age (#12). Also, scientists are well aware that water vapor traps more heat than CO2, but water vapor is simply a feedback mechanism that increases in response to warming (#11). So CO2 is the ultimate cause. Finally, this is not “just a theory” (#13). The science of climate change has been extremely rigorously tested, and its predictions have consistently come true (#5, 7, 9, and 22).

Look, this is happening, people. We know that it is happening. We are already seeing the consequences, and we know that it is our fault. The scientific community stopped debating about whether or not we were causing climate change years ago, and it is about time that the general public stopped debating it as well.

In closing, I want to leave you with a logical proof that we are causing climate change. According to the rules of logic, if you want to reject this argument, then you must either discredit one of my premises or show that I have committed a logical fallacy. If you cannot do one of those two things, then you must accept the conclusion that we are causing the planet to warm. To do otherwise would be illogical.

  1. CO2 traps heat and is largely responsible for regulating our climate
  2. When you increase something that traps heat, more heat will be trapped
  3. We know that increasing the CO2 results in more heat being trapped from both laboratory experiments and past climate data
  4. We have greatly increased the CO2 in the atmosphere
  5. The earth is trapping more heat now than it used to, and this increase matches the increase in CO2.
  6. Therefore, we are causing the climate to warm

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Basic Statistics Part 5: Means vs Medians, Is the “Average” Reliable?

To many people, this may seem like the most boring topic in the world, but it is actually vitally important not only for understanding scientific results, but also for understanding much of the data that we are presented with on a daily basis. We are constantly confronted with claims about the “average” from friends, scientists, doctors, politicians, etc., but in many cases, those data are being presented in a misleading or even deceptive way, and you should be able to spot that deception. For example, during the first calendar year of my blog, my posts were viewed an average of 9,272 times per post. When faced with a number like that, you would normally expect that roughly half of my posts had more than 9,272 views, and roughly half had fewer than 9,272 views. In reality, out of 81 posts, only 12 had more than 9,272 views! You see, this data set is not one that can be accurately analyzed using the calculation that most people refer to as the average (what scientists call the mean), so I presented you with an unrealistic view of my blog by using an inappropriate measure of the central tendency of the data. This example is, of course, trivial, but people do this all the time. You can find plenty of scientific publications that made this blunder, politicians frequently cite inappropriate statistics like this, etc. So I think that it is very important for people to understand the different types of data distributions and how they should be analyzed.

 

Means, medians, and modes
For most data sets, we are interested in knowing the central tendency of the data. This is generally accomplished by presenting a single number that summarizes the data and presents a value that you expect most of the data points to be near. Most people do this by presenting an “average.” As all of you hopefully know, you calculate the average value by adding all of the data points together, then dividing by the total number of data points (scientists refer to this statistic as the “arithmetic mean”). This is by far the most common measure of central tendency among the general public, but it is not the only one, and in many cases, it is a horrible statistic to use (more on that later).

The primary alternative to the mean is what is known as a median. For this statistic, you line all of your data points up from smallest to largest, and the median is simply the middle value. For example, if your data points were: 2, 4, 5, 7, and 30, the median would be 5 (in contrast the mean would be 9.6). In situations where you have an even number of data points, you simply take the mean of the middle two. In other words, if your data points were: 2, 4, 7, and 30, then the median would be 5.5 ([4+7]/2).

Finally, we also have the mode. This is simply the value that appears most often. This statistic is generally not appropriate for numerical data because it doesn’t really show you the central tendency in most cases. It is however useful for nominal data. In other words, when you are simply counting things by category. For example, if you wanted to know the most popular brand of car in your neighborhood you might count them all. Now, suppose that you did that and you found 20 Toyotas, 5 Fords, and 6 Chevrolets. You obviously can’t take a mean or median, but you can report that there were more Toyotas than anything else. That’s a mode (again, you can do that with actual numbers as well, but it often doesn’t tell you much). I thought that it was important to explain what a mode was, but for the remainder of this post I really want to focus on means and medians, because they are the ones that often get used inappropriately (especially the mean).

 

Figure 1: This shows three different types of symmetrical data distributions. (A) shows a normal distribution, which is the only type of data distribution for which means are appropriate.

Figure 1: This shows three different types of symmetrical data distributions. (A) shows a normal distribution, which is the only type of data distribution for which means are appropriate.

Data distributions: when can’t you use means?
Now that we have established what means and medians are, we can talk more about when they can and cannot be used, but to do that, we need to talk about data distributions. As I mentioned in the opening paragraph, when we talk about averages, we generally think that roughly half the data points should be above the mean and half the data points should be below it. Indeed, that intuition is correct. In order for the mean to be really useful, that situation should be roughly true, but if you think back to our definitions of means and medians, you will realize that what we have just defined is a median, not a mean. This brings me to the most important point of the entire post: as a general rule, if you are interested in knowing the central tendency of your data, means more informative when the data are normally distributed, but they can be very problematic when the data are skewed to one side. The easiest way to explain what I mean by that is simply to show you, so look at Figure 1A on the right. This is what we call a “bell-shaped” or “normally distributed” data set, and the mean is at 11, which is exactly where we expect it to be (on a side note, for data sets with a perfectly normal distribution, the mean and median will always be the same).

Technically speaking, you can also use a mean anytime that the data have a symmetrical distribution (i.e., if you folded the graph in half, both sides would be the same), but as you can see in Figure 1B and 1C, although you could report a mean, the mean is still not very useful. In Figure 1B, all of the values are equally frequent, so there isn’t really a central tendency, and in Figure 1C, the distribution is bimodal, so there are really two central tendencies. Data sets like either of those are, however, fairly rare, and the far more common alternative to a normal distribution is a skewed distribution (Figures 2 and 3).

Figure 2: This shows a data set that would be normally distributed if it wasn’t for one data point that is way out at 10,000. That data point gives it an extremely long tail, which results in a very inaccurate mean. As a general rule, the longer the tail, the less accurate the mean is. In contrast, medians are affected by the number of data points on the tail, but the length of the tail is irrelevant. Note: I gave this figure and extremely long tail to illustrate how much one huge outlier can affect means, but the distributions in Figure 3 are more typical of what we mean when we say that a distribution is "skewed."

Figure 2: This shows a data set that would be normally distributed if it wasn’t for one data point that is way out at 10,000. That data point gives it an extremely long tail, which results in a very inaccurate mean. As a general rule, the longer the tail, the less accurate the mean is. In contrast, medians are affected by the number of data points on the tail, but the length of the tail is irrelevant. Note: I gave this figure an extremely long tail to illustrate how much one huge outlier can affect means, but the distributions in Figure 3 are more typical of what we mean when we say that a distribution is “skewed.”

When we say that a distribution is “skewed” we mean that it is not symmetrical like a normal distribution. Rather, the data clump on one side with a “tail” stretching off to the other side. We would further describe the graphs that I have illustrated here as either “right-skewed” or “right-tailed” (because the tail is on the right). Skewed data sets like this are extremely common, but you often cannot use a mean to describe them, because the mean gives you a misleading view of the data. For example, in Figure 2, the median is 11, which makes sense based on just looking at the data. In other words, 11 is a good description of the central tendency of that data set, and saying that the median = 11 tells you something useful. In contrast, the mean for that data set is 20.8, which is obviously a terrible representation of the central tendency of that data set. Almost all of the data points are less than 20.8, and that statistic is extremely misleading.

So what’s going on here? Well this data set has an extremely long tail because there was one data point all the way out at 10,000, and if you think about the math behind the mean, it should be obvious that having a single data point that is so much higher than all of the rest will seriously bias a mean (this is the same thing that happened with my blog data). In contrast to the mean, the median will still be robust because it just ranks that data, then selects the middle data point. So it wouldn’t matter if that last data point was ten thousand or ten trillion, the median would be same (in contrast, the mean will keep going up).

Figure 3: This shows two different left-skewed data sets. The more skewed that they become, the less accurate the mean is. Although Figure 2 is technically skewed, when we use that term to describe data, we are usually referring to distributions that look more like this.

Figure 3: This shows two different right-skewed data sets. The more skewed that they become, the less accurate the mean is. Although Figure 2 is technically skewed, when we use that term to describe data, we are usually referring to distributions that look more like this.

From what I have just said, it should make sense that the length of tail and the number of data points on the tail have a huge effect on whether or not you can use a mean. Consider, for example, the two panels in Figure 3. Both are still right-skewed, but not nearly as severely as Figure 2. Indeed, 3A doesn’t look that far from a normal distribution and, in fact, the mean and median are pretty similar (6.5 and 6, respectively). So although the median is a better statistic, the mean is still pretty good. The more skewed that we make it, however, the further that the gap between those two becomes. In 3B, for example, the media is 3, whereas the mean is 4.3.

All of that may have seemed complicated, so let me boil it down to two take home messages. First, means are reliable measures of the central tendency when the data are normally distributed (or at least close to normal), but when the data are skewed and you have many outliers, the median generally gives you a better representation of the data. Second, the more skewed that a data set is, and especially the longer that its tail is, the less reliable the mean becomes.

Note: There are cases in which the typical relationships between means and medians that I have presented do not hold true, but these generally occur for discrete variables rather than continuous variables. A discrete variable is simply one for which there are a finite number of values, such as count data (e.g., the number of individuals per household would be discrete because you can’t have a fraction of a person). Continuous variables are ones for which there are (at least in concept) an infinite number of values (e.g., measurement data). For more information, please read Hippel 2005. Mean, Median, Skew: Correcting a Textbook Rule. Journal of Statistics Education 13.

 

How to tell if means are being used correctly
At this point, you may be wondering how on earth you are supposed to tell when someone is using a mean when they should be using a median, and there are a couple of things to watch out for. First, familiarity with the type of data being worked with is usually very helpful, because if you know something about how those distributions generally appear, you can often intuit what the distribution will probably look like. Let me give you an example. If someone is reporting the mean income for all of the US, do you think that is appropriate? Well, if you know anything about the wealth distribution in the US, then you know that there is a very large lower and middle class, accompanied by a tiny upper class that makes way, way more than the other two classes. Now, picture in your mind what that distribution will look like. You should be picturing a very skewed graph with most people in the low to moderate income categories on the left, and a few rich people way out on a tail on the far right, and, indeed, that is what the distribution looks like. So in that case, people should be reporting medians, not means (note: I am not making any political statements here, I am just using a simple example that most of my readers should be familiar with).

To be clear, I’m not suggesting that you go with your gut instead of actually looking at the data, but background knowledge about the type of data being presented is useful as a first pass filtering mechanism to see if any red flags go up. It is also one of the reasons why it is important to be knowledgeable in a particular scientific field when trying to assess that literature. Knowing what the data sets for a given topic typically look like helps you to spot shoddy statistics.

In cases where you can’t intuit or easily look up the distribution, ranges become really important. If, for example, someone tells you that the mean for something is 100, that isn’t very useful without out also knowing something about the distribution of data around that mean. Standard deviations and variances are generally really useful for that purpose, but for checking normality, the range is far more valuable, because it gives you the highest and lowest values. Suppose, for example, that you were told not only that the mean was 100, but also that lowest value was 10 and that the highest value was 110 (i.e., the range was 10–110). That tells you something very useful about the data, because it tells you that the data has a long left tail (i.e., it is left-skewed), which also tells you that the mean is probably inappropriate.

 

Conclusion
There are several key things to take away from this post. First, people often report averages (means), but it is often inappropriate to do so, and you should be cautious of them. Means only work when the data are fairly close to a normal distribution, and when the data sets are skewed, you need to use medians, not means. As a result, when someone presents you with a mean, you should think about the distribution of the data and look at other pieces of information such as the range.

As a fun concluding exercise, I want you to evaluate two claims:
Claim 1: The average number of followers per Twitter account is 208.
Claim 2: The average height of women in the USA is 163 cm.

The goal here is to assess whether or not the mean is reliable for those two data sets. So, for both claims, I want you to think about how you expect the data to look. Think about the types of accounts that often exist on Twitter and the range of human heights, and see if you can intuit what the data sets will look like. Then, actually look into the data a bit and see if you were right. This is a very easy exercise that won’t take you more than a few minutes, but it is the type of skepticism that you should apply to all data sets, so I think that it will be useful for you to actually work through this mentally. Also, yes, I did just assign you homework from a blog post.

The answer for twitter is here, and for height you can just check good old Wikipedia for a comparison of the mean and median.

Related Posts

Note: This is the second time that I have posted this basic article. I removed the first version almost as soon as I posted it because someone pointed out that I made several errors and over-generalizations. I did not immediately have time to edit the post, so I simply removed it rather than furthering the spread of misinformation. I have now corrected the post, and I apologize profusely for my mistakes and appreciate having them pointed out to me. I try very hard to write accurate and informative posts, but I am only human and do make mistakes, which is why it is important to fact check everything that you read and hear (including from me). Thank you for not letting me get away with shoddy work.

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