Sinking Noah’s Ark Part 1: Introduction

Posted on July 11, 2016 by Fallacy Man
I'd be far more impressed with the Ark Encounter if it had been built by four people with hand tools. Image via Why Evolution is True

I’d be far more impressed with the Ark Encounter if it had been built by four people with hand tools. Image via Why Evolution is True

There has recently been a lot of hype over Answers in Genesis opening its latest attraction, the “Ark Encounter.” This life sized “replica” of Noah’s Ark is intend to be a tool to help spread the message of young earth creationism, which would be all well and good if it wasn’t for the fact that young earth creationism is scientifically ludicrous and demonstrably false. Therefore, in this series of posts, I want to go over several of the many lines of evidence which clearly show that a recent world-wide flood did not occur, as well as dealing with some of creationists’ arguments in support of the flood. Before I delve into those arguments, however, I want to use this post to explain why the flood is such an important topic. I often find that skeptics and scientists tend to ignore the flood when debating creationists, but I think that we should actually be focusing on it, because as I will demonstrate, the flood is absolutely essential to creationism and it is the easiest of their positions to convincingly debunk.

Note: As always, I want to be absolutely clear that I am not attacking religion. This blog is about science, not religion, so it is not my intention or purpose to debate whether or not there is a god, whether or not Christianity is true, etc. However, any time that a religion makes a claim about the physical universe, it has entered into the realm of science, at which point it comes under my crosshairs. There are plenty of people who both believe the Bible and accept that evolution is true, the earth is old, etc. (see posts here and here). So if you can reconcile your faith with science, then you and I won’t really have any problems. It is not, however, acceptable for you to allow your faith to trump science, and that is what I take issue with. So, for any creationists reading this, I am not asking you to abandon your faith. Rather, I am asking you to take a critical look at what you believe and make sure that it is consistent with scientific facts. Indeed, you already use science to interpret many passages of the Bible, so there is no reason for you not to apply those same hermeneutical principles to Genesis.

 What do creationists believe?
I often see skeptics wasting their time attacking straw men; therefore, I think that it is important at the outset to state exactly what it is that creationists believe regarding the flood. According to creationists, roughly 4,500 years ago, God grew so tired of mankind’s depravity that he decided to wipe out all humans (except Noah and his family) as well as all land animals (I have to wonder what the animals did to deserve that, but that’s beside the point). So, God told Noah and his sons to build a massive ark, into which God sent two of each “kind” of land animal (birds, mammals, reptiles, and possibly amphibians depending on which creationist you talk to). God then caused a massive storm during which it rained for 40 days straight and the entire planet was covered in water (even the highest mountains were covered). The flood rapidly buried many plants and animals in sediment, while others tread water before eventually drowning and being buried as sediment fell out of the water. Thus, the flood formed the vast majority of fossils as well as the geological layers that we see today (e.g., all of the layers of sedimentary rock that you see at places like the Grand Canyon were supposedly deposited by the flood). In total, the flood lasted for roughly a year, and once it was over, Noah released the animals from the ark, and they went forth to breed and repopulate the earth (and apparently not instantly eat each other).

That may sound simple, but there are several crucial points that need to be emphasised. First, creationists believe that all of the major kinds of land animal were alive prior to the flood and were included in the ark. So ever major group of animals that has ever lived (including dinosaurs) was alive before the flood and was included in the ark. That is going to become really important in coming posts.

Second, you have to understand what creationists mean by the term “kind.” According to them, Noah did not actually have two of each species on the ark. Rather, he had two of each kind on the ark. To them, a “kind” is roughly the same as the scientific term “family.” Thus, Noah did not have two tigers, two lions, two bobcats, two leopards, etc. Rather, he had two cats, because according to them, the cat family (Felidae) represents one kind. Following the flood, those “kinds” evolved into the species that we see today. This “kind” argument is actually extremely problematic for a number of reasons (for example, as I explained here, it is totally logically inconsistent), but it is nevertheless what they think, and it is important to understand that so that you don’t commit a straw man fallacy. In other words, I often see skeptics arguing that the flood is impossible because two of each of the millions of species of animals that are alive today could not have possibly fit onto the ark, but creationists don’t think that all of the species were there; rather, they think that all of the kinds were there. I have seen a lot of different estimates from them regarding how many “kinds” would have been present, but the numbers are usually less than 30,000. Again, you’re welcome to debate this notion of kinds, and in later posts I will show why two of each “kind” could not possibly have produced all of the species that we see today, but it is nevertheless what creationists believe, and you have to address their actual beliefs, not straw men.

The importance of the flood to creationism
If you want to debate creationists, you have to understand the flood, because it is a cornerstone of their view. For creationists, a literal world-wide flood is a “get out of jail free” card that they use to dismiss just about any evidence that the earth is old/evolution is true. Ask a creationist why dinosaurs aren’t around anymore, and the answer will be, “they couldn’t survive the environment after the flood.” Ask them where all of the fossils came from if the earth isn’t millions of years old, and they will say, “they formed during the flood.” How did the Grand Canyon form? “The flood.” How did varves form? “The flood.” etc.

To be clear, these arguments are horribly flawed and almost all of them commit ad hoc fallacies (more on that in a minute), but the point is that creationists rely heavily on the flood as their means of explaining away scientific evidence. So, by showing that the flood is impossible, you kick out a critical leg of creationism, and you defeat young earth creationism as a whole. In other words, if the flood did not occur, then creationists have no way to explain the fossil record, geology, etc.

Why debate the flood?
Beyond the importance of the flood to creationists, there is another really important reason why skeptics/scientists should focus on it. Namely, it is actually falsifiable. You see, many creationist beliefs simply cannot be disproved via science. Take, for example, the notion that God specially created all living things. We can look at the fossil record, genetics, biogeography, vestigial structures, etc. and see that they exactly match the predictions of evolution, but creationists can always respond to that simply by saying, “well God made it that way.” That statement is inherently unfalsifiable, because no matter how closely our observations match the predictions of evolution, it is always possible that for some unknown reason a cosmic being decided to create life in such a way that it looked like it had evolved. To be clear, that belief is not even remotely rational (in fact it commits another ad hoc fallacy), but it is not technically falsifiable because it invokes the supernatural, and the supernatural is outside of the realm of science.

In contrast, a literal world-wide flood is falsifiable because it would have been physical. Although creationists argue that God caused the flood, they generally argue that he simply triggered it, and the flood itself was an entirely physical event. As such, it is entirely within the realm of science and we can test it and potentially falsify it. In other words, if the flood occurred, then it should have left behind distinct pieces of evidence, so we can look at the evidence and see if it matches the predictions of the flood. This makes the flood a much better target for debate than creationism more generally.

Ad hoc fallacies
Ad hoc fallacies are one of the most common tactics used by creationists, and you will see them a lot throughout this series, so I want to spend a few minutes explaining what they are and how to spot them. These fallacies are generally responses to arguments rather than arguments themselves, and they have two key characteristics. First, they are designed entirely to plug a hole in an argument. In other words, they aren’t part of a broad conceptual framework nor are they things that you would intuitively expect. This brings me to the second key characteristic: you wouldn’t accept an ad hoc argument unless you had already accepted position that it was designed to save. In other words, they are invented solutions, and there is no reason to accept them other than that they let you maintain a view that you are fond of. If you are familiar with the literary construct known as “deus ex machina,” this is basically the debate equivalent. It is an unsupported solution that is pulled out of thin air.

Let me give you an example. Suppose that someone claims that a ghost is reliably being encountered in a certain house. So I go to the house with the person several times and never detect anything that could possibly be considered to be a ghost. When I ask my guide why this supposedly frequent ghost never shows up when I’m there, he/she responds, “it doesn’t like to come out in the presence of skeptics.” Do you see how that works? There is no logical reason to accept that response. It is not something that you would intuitively expect to be true. Rather, it is a made-up solution that does nothing other than “solve” a problem in the ghost hunter’s position, and it is a solution that I would never accept unless I was already convinced that ghosts were real (this is very similar to question begging fallacies, and they often occur simultaneously). This example also illustrates another common characteristic of ad hoc fallacies. Namely, they are often impossible to disprove. In other words, the claim that the ghost only shows up around believers is impossible to properly test, because people who weren’t already convinced that the ghost was real would need to be there to unbiasedly confirm or refute its existence.

Creationists commit this fallacy all of the time, so you should watch out for it, and there are two easy tests to detect it. First, examine whether or not a given response is backed up by actual data. For example, to explain the size of current coral reefs, creationists often claim that in the past they grew at many times their current growth rate. The problem is that there is absolutely no evidence that such a growth rate occurred or is even physically possible. Thus, this is a made-up solution. The second test is simply to ask yourself whether or not someone would accept that response if they were not already convinced of the thing being defended. For corals, the answer is again a resounding “no.” There is no scientific reason to think that corals used to grow substantially faster than they do now, so the only reason that anyone would ever accept that response is if they already thought that the flood was true/the earth was young, and they wanted to maintain that belief.

Conclusion/future posts
Now that we have the important introductory material out of the way, we can start on the actual arguments themselves, and there are many of them. In coming posts, I will be explaining the fossil record and geology, varves, more details on corals, genetics, and multiple other lines of evidence which clearly show that a world-wide flood did not occur. In addition to refuting the flood, many of these arguments also provide strong evidence that the earth is old and/or the theory of evolution is correct. If you are a creationist reading this, then I have just one simple request for you: I want you to seriously consider the possibility that you might be wrong. Many creationists, like Ken Ham, openly admit that no amount of evidence will ever make them change their minds, and if that is your position, then there is really no point in you reading any further. If, however, you a really interested in knowing what is accurate and factual, then I encourage you to lay aside your preconceptions and carefully consider the evidence and logic that I will be presenting.

If you are impatient and don’t want to wait for my posts, I recommend reading Moore. 1983. The impossible voyage of Noah’s Ark. Creation Evolution Journal 4:1–43. It is an excellent essay on many of the problems with the flood story.

Other posts in this series

 

Posted in Science of Evolution | Tagged , , , , | 2 Comments

If cannabis and vitamin B17 kill cancer, why aren’t they approved by the FDA? Let me explain

Posted on July 4, 2016 by Fallacy Man

cancer curesIt seems like hardly a week goes by without some news article claiming that a simple cure to cancer has been found. Similarly, social media is full of images like the one to right claiming that cannabis oil, vitamin B17, and a host of other things can kill cancer cells. However, more often than not, these claims are extremely misleading because the studies that are being referenced are usually in vitro studies (i.e., studies on cells in a petri dish) or, at best, animal trials, and both of those designs are extremely limited. As I will explain in this post, killing cancer cells in a petri dish, and safely killing cancer cells in a human body are two extremely different things. Indeed, many chemicals appear promising in laboratory trials but are later shown to be either dangerous or ineffective in clinical trials on humans. So you should be very skeptical of the news headlines, and you should be slow to jump to extreme conclusions (e.g., arguing that it’s a massive conspiracy by the government and pharmaceutical companies).

There are many types of cancer
The first thing to realize about any claim regarding a “cure for cancer” is that cancer is not a single disease. There is no one type of cancer. Rather, there are many different types of cancer, each of which behaves differently. Therefore, even if you successfully demonstrated that something could cure one type of cancer, you would not have demonstrated that it is effective against any of the other forms of cancer.

This is especially important to understand with regards to in vitro trials, because they are inherently limited by their use of cell lines. A “cell line” is a clonal population of cells that is maintained in a laboratory. To start a cell line, scientists take a single cell (e.g., a cancer cell) and grow it in the lab, resulting in it duplicating many times. This is very useful because the duplicates are exact copies of the original (barring any mutations). Thus, scientists can use cell lines to completely eliminate most confounding factors, and two different scientists working in different labs can do comparable research by using the same cell line.

Nevertheless, despite the obvious benefits of cell lines, they are also problematic because they limit the scope of your conclusions. Imagine, for example, that scientists find that chemical X kills cancer cell line Y in the lab. All that would mean is that X kills one particular line of one particular type of cancer. It may or may not work on any other cell lines or any other types of cancer. So you have to keep that in mind when you hear that a new study found a chemical that killed a particular line of cancer cells. The study may be right, but its results may not apply to anything beyond that particular cell line.

Killing cells in a petri dish and kill cells in a human are not the same thing
The next important point is that there is a world of difference between effectively killing cancer cells in a petri dish and effectively killing cancer cells in the human body. In the lab, you can put a large dose of the chemical directly on the cells, whereas in the human body, you have to either inject or ingest it, which is instantly going to dilute it. Further, your liver and kidneys will likely try to filter the chemical out, and it may interact with any one of a thousand other chemicals in your body. Remember, everything in your body is made of chemicals, and (with the exception of catalysts) chemical reactions change the chemicals by breaking or forming bonds. So you may have a chemical that is extremely effective in the lab, but in the body, it quickly reacts with other chemicals and changes into a chemical that is not at all effective at killing cancer cells.

The fact that something kills cancer cells does not mean that it is safe to use. Image via xkcd

The fact that something kills cancer cells does not mean that it is safe to use. Image via xkcd

Is it safe?
This is probably the most important point in this post. For a “cure” for cancer to really be effective, it has to both kill cancer cells and be safe for your other cells. Finding things that kill cancer cells is easy, but finding things that kill cancer cells without harming your other cells is very difficult. For example, flame throwers kill cancer cells, but that doesn’t make them an effective treatment. Similarly, gasoline will kill cancer cells, but I don’t recommend drinking it. Alcohol will also kill a plate of cancer cells, but in humans, it actually causes cancer! Laetrile (commonly known as vitamin B17) is another great example of this. There is some evidence that it can kill cancer cells in the lab, but it does so by releasing cyanide, which will kill your normal healthy cells just as quickly as it kills cancer cells.

This brings me to another important point: cancer is very difficult to treat because it is part of your body. Developing a drug that safely kills something like bacteria is comparatively easy because bacterial cells are biochemically very different from human cells. So to kill bacteria, you just have to develop a chemical that will interfere with biochemical pathways that are found in bacteria but not found in humans. Cancer is much more challenging because the cancer cells are simply mutated versions of your own cells. As such, they have most of the same basic biochemical pathways as your other cells. This makes it exceptionally hard to find a safe treatment, because most chemicals that kill cancer cells will also kill normal, healthy cells. This is a big part of why it is so hard to cure cancer, and it is a very important reason why you should be skeptical of claims that someone has found a cure for cancer. Unless they can explain exactly how that cure targets cancer cells without targeting normal cells, you probably shouldn’t believe them.

Finally, we have to remember that the dose makes the poison as well as the cure. So for a chemical to be an effective treatment, it is going to have to kill the cancer cells at a dose that is below the dose at which it becomes dangerously toxic to humans. Indeed, this is the balance that doctors try to find with our current treatments of radiation and chemotherapy. Cancer cells divide very rapidly, and those treatments are particularly good at attacking dividing cells, so they tend to do more harm to cancer cells than to your normal cells, but there is still collateral damage. In other words, the idea is to find a balance between having just enough of the treatment to kill the cancer cells, without killing too many of your healthy cells. This is extremely important to understand, because although some of the “alternative” cancer treatments might kill cancer cells, they probably kill your healthy cells as well, and because they haven’t been well-studied, we don’t know what the correct dose is. In other words, don’t assume that something is safe just because it has the label of “alternative medicine.” As I said earlier, B17 actually produces cyanide. So “alternative” treatments may not be safe, and they usually aren’t even treatments.

hierarchy of scientific evidence, randomized controlled study, case, cohort, research designThe hierarchy of evidence
The overarching point that I am trying to make here is that you should be skeptical about claims regarding cancer cures (and medicine and science in general). Whenever you hear a claim that something cures cancer, you need to take a good look at the evidence to see whether or not that claim is justified. More often than not, the claims are based on anecdotes, in vitro trials, and animal trials. I explained the problems with anecdotes at length here, and I have spent most of this post on in vitro trials, but animal trials suffer many of the same problems. Mice and rats are biochemically different from humans, and drugs often behave very differently in animals than they do in humans. For example, although avocados are generally considered to be safe for humans, they can be toxic to birds, mice, rabbits, rats, sheep, and a variety of other animals.

This is especially important for diseases like cancer, because, once again, there are many types of cancer that all behave differently, and cancers in rats may not be representative of cancers in humans. Further, just like the cell lines for in vitro trials, animal trials generally used inbred strains of mice/rats. In other words, family groups have been inbred to the point that they are all genetically identical (or at least nearly so). So, just as with in vitro trials, it is very difficult to generalize from animal trials. Indeed, it is very often the case that a drug appears promising in in vitro/animal trials but utterly fails on humans.

Because of all of the problems that I have been talking about, animal trials and in vitro trials are at the bottom of what we call the “hierarchy of evidence.” Not all experimental designs are equal, and some give much more reliable and applicable results than others. Animal trials and in vitro studies are considered to be very unreliable, and, therefore, they are intended simply as starting points for future investigation, and you should never draw strong conclusions from them. In other words, the more robust experimental designs (like cohort studies and especially randomized controlled trials) tend to be very time consuming and expensive. Therefore, scientists use the lower quality study designs to identify the drugs that are interesting enough to merit further investigation. So animal trials/in vitro studies are often the basis for starting a large clinical trial, but you should always base your conclusions on the results of the large clinical trials.

The point is that before you accept that something cures cancer in humans, you need to look at the studies to see if they actually have the ability to reach that conclusion. In almost every case, they don’t. For example, systematic reviews of B17 have found that there is insufficient evidence to conclude that it is either safe or effective at treating cancer in humans (Milazzo et al. 2007, 2015). Similarly, we just don’t have any good clinical data to suggest that marijuana is effective at curing cancer in humans. So in answer to the question of why the FDA hasn’t approved treatments like these, it is quite simply, because we don’t have compelling evidence that they are either safe or effective in humans. The whole point of the FDA is to make sure that treatments are both safe and effective before they are approved for general use, but that evidence is completely lacking for alternative cancer treatments.

Conclusion
In short, most of the time when you hear someone say that a chemical cures cancer, they are referring to an in vitro study, but those studies are extremely problematic. They can only show that a given chemical kills a particular cell line for a particular type of cancer in a petri dish. They don’t show that the chemical kills cancer cells generally, nor do they show that it is safe for humans, or that in the human body it will be able to reach the cancer cells without either being filtered out by the liver and kidneys or interacting with other chemicals. There are countless chemicals that kill cancer cells in a petri dish, but that doesn’t mean that they are either safe or effective at treating cancer in humans. Indeed, there are many chemicals, such as gasoline and arsenic, that will kill cancer cells in the lab, but that doesn’t mean that it is a good idea to ingest them. In fact, many of the “cures” for cancer are very dangerous to healthy human cells (e.g., vitamin B17 works by producing cyanide and MMS is an industrial bleach). So the reason that these “alternative treatments” aren’t FDA approved is simply that they have not been shown to be either safe or effective in humans, and it is the FDA’s job to make sure that treatments are not approved until they have met those two criteria. The point is that whenever you hear a claim about a cure for cancer, you should be skeptical, and you should check the sources and see if the claim is actually backed up by high quality studies that are capable of reaching that conclusion.

Related posts


Literature Cited

Milazzo et al. 2007. Laetrile for cancer: a systematic review of the clinical evidence. Supportive Care in Cancer 15:853–595.

Milazzo et al. 2015. Laetrile treatment for cancer. Cochrane Database of Systematic Reviews.

Online references
Alcohol and Cancer Risk. National Cancer Institute. Cancer.gov. Accessed 3-July-16.

Avacado. The Merck Veterinary Manual. Merkvetmanual.com. Accessed 4-July-16.

Laetrile/Amygdalin (PDQ)-Patient Version. National Cancer Institute. Cancer.gov. Accessed 3-July-16

Laetrile (amygdalin, vitamin B17). Cancer Research UK. Cancerresearchuk.org. Accessed 3-July-16.

 

Posted in Nature of Science, Vaccines/Alternative Medicine | Tagged , , , , , | 9 Comments

Why are there so many reports of autism following vaccination? A mathematical assessment

Posted on June 28, 2016 by Fallacy Man

The idea that vaccines cause autism is one of the most persistent myths that I have ever encountered, and it seems that no amount of evidence will ever cause it to disappear. Indeed, I recently wrote a lengthy post in which I thoroughly reviewed the scientific literature on this topic, and I showed that there are no high quality studies supporting this myth, but there are multiple very large studies that debunked it. Nevertheless, many people responded to the post by insisting that vaccines must cause autism because there are so many cases of parents reporting the onset of autism shortly after vaccinating. They were adamant that these anecdotes could not be chance results and must mean that vaccines cause autism. To quote one commenter,

“How can you sit there and say that observing an adverse reaction AFTER a vaccine is given doesn’t mean it was the vaccine? So, what…? It’s just a random coincidence? I think not.”

Given how common this argument is, I want to look at these anecdotes and what they actually mean, and, as part of that, I want to actually do some math and calculate the probability of a child developing autism shortly after being vaccinated if the vaccine isn’t responsible. In other words, I want to calculate the odds of observing autism “AFTER a vaccine is given” just by “random coincidence.”

The problem with anecdotes
Anecdotes are extremely problematic for multiple reasons. I’ve explained them in detail before, so I will just discuss the core problem here. Namely, using anecdotes as evidence of causation commits a logical fallacy known as post hoc ergo propter hoc. This fallacy occurs whenever you say “X happened before Y, therefor X caused Y” (e.g., “Billy was vaccinated shortly before showing the first signs of autism, therefore the vaccine caused the autism”). The problem is that just because one thing happens before something else does not mean that the two are related. It could, in fact, be a complete coincidence (more on that later). Many people often become very indignant when I say that (as the commenter that I quoted did), but these aren’t arbitrary rules that I have made up. This is just how logic works. The fact that X happened before Y does not in any way demonstrate that X caused Y. You need additional information (such as knowledge of confounding factors) before you can assume that they are related.

Here is an example that I often like to use. Imagine that I fill my car with fuel and it breaks down a mile later. Can I assume that the fuel was bad and that’s what made my car break down? Obviously not. There are lots of parts on a car that can break, and it is entirely possible that one of them just happened to break shortly after fueling up. In other words, it could my be a complete and total coincidence. Think about it, thousands of people fill up their cars every day, and thousands of people break down every day. So it is inevitable that there will be lots of cases where someone just happens to break down right after filling up. The fact that fueling up happened before breaking down does not mean that fueling up caused me to break down, and that exact same logic applies with vaccines and autism.

To give another example, imagine that someone takes a new medication, then has a heart attack the next day. Can we conclude that the medicine caused the heart attack? No, we can’t. Heart attacks are extremely common, so it shouldn’t be surprising that, just by chance, some people will have heart attacks shortly after taking a medicine, even if the medicine didn’t cause the heart attack.

How to study anecdotes
Although anecdotes are not very useful as evidence of causation, they can be very useful as starting points for scientific investigation. To go back to my heart attack example, if there were multiple reports of people having heart attacks shortly after taking the medication, doctors and scientists would take those reports seriously, and they would use them as the justification for doing a study. This is a crucially important point: the only way to actually know whether or not the medication causes heart attacks is to compare heart attack rates in people who do and do not take the medicine (or at least compare different doses) while controlling confounding factors. Only then, once you have controlled the confounding factors, can you conclude that the relationship is causal rather than simply being a coincidence. In other words, you have to control all other possibilities and compare two treatment groups before you can conclude that the medicine is the cause. Otherwise, you can never be sure that it’s not just a coincidence.

Now, if we go back to vaccines and autism, we find an identical situation. The fact that many parents have observed autism shortly after a vaccine does not in itself indicate that vaccines cause autism, but it does give scientists something to investigate. In other words, to test those anecdotes and find out whether they are arising from chance or from a causal relationship, we need to compare autism rates among children who did and did not receive a given vaccine. Scientists have, in fact, done that numerous times, which is why anti-vaccers’ comments are a bit strange. I constantly encounter anti-vaccers who insist that scientists aren’t taking parents seriously and haven’t examined the reason that autism often seems to follow vaccines. For example, someone made the following comment on my post about the scientific studies on vaccines and autism.

“In other words, there is a high enough number of parents reporting an immediate and dramatic and permanent change in the behavior of a child, that the link was established. This is the core of what needs to be understood and studied. It also appears that it continues to be ignored. In other words, until we study why these reports exist, and in the numbers that they exist, and focusing on just those particular children, we have not demonstrated the science needed to make a conclusion.”

Notice how the commenter suggests that scientists have ignored parents reports and aren’t studying those reports. That is a rather bizarre accusation given that we have multiple very large studies on whether or not vaccines cause autism. In other words, we know that the anecdotes are wrong because scientists took parents’ concerns seriously, carefully tested vaccines, and repeatedly found that there is no relationship between vaccines and autism. Further, just to be clear, scientists didn’t just do one small study then call it quits. Rather, they have looked it this from multiple angles such as testing different vaccines, examining the effects of age at vaccination, studying children that are at a high risk of autism, testing different doses of vaccines, etc., and some of those tests have been enormous (the largest used over 1.2 million children!). So scientists absolutely took parents’ concerns seriously, but it turned out that the parents’ concerns were unnecessary. In other words, the issue isn’t that scientists are ignoring parents. Rather, the issue is that they didn’t find the results that anti-vaccers wanted them to find. Scientists have very carefully examined vaccines to see if the anecdotes actually represent a causal relationship, and anti-vaccers are simply refusing to accept their results.

See the text for sources and more details.

See the text for sources and more details.

Looking at the math
Despite the fact that anecdotes can’t demonstrate causation and the fact that multiple studies have shown that the anecdotes are in error, many people continue to insist that the temporal relationship between vaccines and autism simply can’t be a result of chance. So let’s look at that with some simple math. What I want to do is calculate how many times we expect the first signs of autism to closely follow vaccines if vaccines don’t actually cause autism.

Note: I will explain the math below, but for your convenience, I have laid it all out in the image above.

According to the CDC, there are roughly 3,988,000 children born in the US each year  (Hamilton et al. 2015). This gives us a nice annual cohort that I want to follow. Over 90% of children in the US receive their full vaccine schedule (Elam-Evans et al. 2014), so that gives us 3,589,200 vaccinated children per annual cohort (0.9*3,988,000). Now, in the US, 1 in 68 children develop autism (Christensen et al. 2016), so if vaccines and autism are not in any way related, we expect that 1 in 68 of our 3,589,200 children will develop autism. Thus, we should have 52,782 children each year who have autism and are fully vaccinated (3,589,200/68).

In roughly 80% of cases, parents first notice the signs of autism prior to a child’s second birthday, and usually not until after around 6 months old (Giacomo and Fombonne 1998). Therefore, for our 52,782 children, there should be 42,226 for which parents first noticed the signs of autism between 6 and 24 months old (0.8*52,782). Now, let’s assume for a minute that there is an equal probability of the first signs of autism appearing on any day during that period (I’ll talk about that assumption in a minute). As a result, we expect 77 children to show the first signs of autism on any given day (42,226 children/548 days).

Now, let’s bring all the pieces together. According to the recommended CDC vaccination schedule, most children receive vaccines on at least 2 days between age 0.5 and 2 years. So if we use that as a conservative estimate, then each year, we expect 154 children (77 cases*2 days) in the US to show the first signs of autism within 24 hours of being vaccinated just by chance (that number goes up dramatically if you spread the vaccines out over more than 2 days). Similarly, each year 1,079 children should show the first signs within 1 week of receiving a vaccine, and 4,623 should show the first signs within 1 month of receiving a vaccine. In other words, so many children develop autism and so many children are vaccinated, that even though vaccines do not cause autism, we still expect there to be hundreds or even thousands of cases where the signs of autism were first noticed shortly after receiving a vaccine (note again that this is the same situation as my fuel example earlier). So we do, in fact, expect there to be lots of coincidences where the detection of autism just happened to follow vaccination.

To be fair, my calculations obviously include two major assumptions, so let’s talk about those for a second. First, I assumed that vaccination only took place on two days, but that is actually a conservative estimate. If everyone vaccinated over 3 days, for example, then we expect there to be 231 annual cases of autism appearing within 24 hours of a vaccine.

Second, I assumed that the rates of autism detection were constant over the period we were talking about. That assumption is clearly false. In actuality, the detection probability goes up over time, but given that one of the days of vaccination is usually early in our time frame and the other is late, that should largely balance out. Also, realize that right now we are quibbling over the exact numbers that I calculated, rather than my central result. In other words, the exact numbers are almost certainly off, but the central point stands (i.e., we expect there to be lots of cases where, just by chance, autism is detected shortly after a vaccine).

Additionally, we would actually expect the odds of a parent noticing the symptoms of autism to skyrocket shortly after a vaccine is administered. Many parents are very concerned about a vaccine harming their child, and, as a result, they will tend to watch their children very closely after vaccinating them (even if they don’t consciously realize that they are doing so). Thus, they are far more likely to notice an early sign of autism that they might have missed if they hadn’t been watching their children so closely. To give an analogy, after people buy a new car, they often start seeing that model and paint job everywhere, but that model isn’t actually any more abundant than it was before, it’s just that their brains notice it because they are thinking about it (consciously or subconsciously). Even so, you are far more likely to notice an early sign of autism if you are worried about it. So in actuality, my numbers are likely underestimates rather than overestimates.

Finally, you may be thinking, “but those numbers are lower than actual number of cases of autism that follow vaccination each year,” to which I have several replies. First, again realize that these numbers are not precise and the true values are likely much higher. Second, where are your sources that the rates are much higher? I’m betting that your sources are simply collections of anecdotes, in which case, you don’t actually have any idea how often autism is detected within a few days of being vaccinated. It may seem much higher than it really is simply because you’re getting your information from internet echo chambers. This brings me to my final and most important point. I completely agree that this argument does not prove that vaccines don’t cause autism. Rather, it simply shows that we expect there to be lots of cases where the detection of autism follows vaccination just be chance. The only way to actually know whether the true rates are higher than the rates expected just by chance is (you guessed it) to do a large study, which, once again, is exactly what scientists have done multiple times.

Note: please read this post before arguing that we need a fully vaccinated vs. full unvaccinated study.

But aren’t autism rates increasing?
As I wrote this post, I could already hear peoples’ keyboards furiously clicking away and arguing that I must be wrong because autism rates have increased over time. So let’s talk about that for a minute. First, as I explained here, at least a large portion of the increase has been due to diagnostic changes, rather than an actual increase (i.e., people who would not have been considered autistic 20 years ago are considered autistic today;  Rutter 2005; Taylor 2006; Bishop et al. 2008; Baxter et al. 2015; Hansen et al. 2015). Second, that doesn’t change the math nor does it refute the numerous large studies that failed to find any evidence of vaccines causing autism. Remember, correlation is not the same as causation. Even if actual autism rates truly are going up, that wouldn’t mean that vaccines are the cause, and in fact, we know that vaccines aren’t the cause because we have so thoroughly studied this.

Summary
In conclusion, anecdotes cannot demonstrate causation, but they can be useful as starting points for further research. In the case of vaccines and autism, that research has been thoroughly conducted, and it has overwhelmingly shown that vaccines do not cause autism. Therefore, the reports of autism being detected shortly after vaccination must be arising by chance. Although many people protest that notion, it is not at all surprising given the number of children who receive vaccines and the number who develop autism. Further, I have mathematically demonstrated that even if vaccines do not cause autism, we expect there to be a large number of cases where, just by chance, autism is detected shortly after the administration of a vaccine.

Related Posts

Literature Cited

  • Baxter et al. 2015. The epidemiology and global burden of autism spectrum disorders. Psychological Medicine 45:601–613.
  • Bishop et al. 2008. Autism and diagnostic substitution: evidence from a study of adults with a history of developmental language disorder. Dev Med Child Neurol 50: 341–345.
  • Christensen et al. 2016. Prevalence and characteristics of autism spectrum disorder among children aged 8 years — autism and developmental disabilities monitoring network, 11 sites, United States, 2012. CDC Morbidity and Mortality Weekly Report 65:1–23.
  • Elam-Evans et al. 2014. National, state, and selected local area vaccination coverage among children aged 19–35 months — United States, 2013. CDC Morbidity and Mortality Weekly Report 63:741–478.
  • Giacomo and Fombonne 1998. Parental recognition of developmental abnormalities in autism. European Child and Adolescent Psychiatry 7:131–136.
  • Hamilton et al. 2015. Births: Final Data for 2014. National Vital Statistics Reports. CDC.
  • Hansen et al. 2015. Explaining the increase in the prevalence of autism spectrum disorders: the proportion attributable to changes in reporting practices. JAMA Pediatrics 169:56–62.
  • Rutter. 2005. Incidence of autism spectrum disorders: changes over time and their meaning. Acta Paediatr 94:2–15
Posted in Vaccines/Alternative Medicine | Tagged , , , , | 72 Comments

The nirvana fallacy: An imperfect solution is often better than no solution

Posted on June 20, 2016 by Fallacy Man

In this post, I want to briefly explain and discuss a logical blunder known commonly as the “nirvana fallacy.” This fallacy occurs when you suggest either that a solution should not be used because it is imperfect or that a solution should not be used because there is some underlying issue that is not being addressed, but you fail to provide a plausible alternative. That may seem a bit confusing at first, so I will use several examples that are commonly used by opponents of science.

Let’s start with one of the most basic and most obvious examples. Anti-vaccers often like to claim that we should not vaccinate because vaccines aren’t 100% effective. This is an extremely clear cut instance of the nirvana fallacy, because the fact that something isn’t 100% effective does not mean that we should not use it. Partial effectiveness is still better than no effectiveness. Indeed, almost nothing is 100% effective. For example, seat belts, helmets, condoms, parachutes, etc. are all less than 100% effective, but they are still very useful. Even so, a life-saving medical marvel like vaccines doesn’t need to be 100% effective to be useful, because every life that is saved is important.

Now let’s look at a slightly more complex example: the peer-review system. This is the system that scientific papers have to pass before being published, and it is admittedly imperfect. Indeed, I have devoted numerous posts on this blog to problems with it (for example here, here, here, and here). This has led some to suggest that it is worthless and should be abandoned entirely. In reality, however, an imperfect quality control system is still better than no quality control system at all. For example, we can all agree that a quality control system at a candy bar facility that limits cockroach legs to one per every ten chocolate bars is better than no quality control system at all (note: those aren’t actual statistics). Even so, the peer-review system is imperfect, and bad papers do sometimes get through, but an awful lot of bad papers never make it.

This brings me to an important point about nirvana fallacies: if you are going to argue that something should be abandoned because it is imperfect, then you must simultaneously propose a more effective alternative. To go back to my chocolate bar example, there would be nothing wrong with saying, “we should stop using the current method that limits cockroach legs to 1 per 10 bars, and switch to method B, which limits legs to 1 per 20 bars.” It is, however, invalid to simply say, “the current quality control system doesn’t stop every cockroach leg (i.e., it isn’t 100% effective), therefore we should remove the quality control altogether.” Even so, if you can propose a better alternative the current peer-review system, then by all means do so, but it is ridiculous to argue that we should either let any “study” pass as valid science or just abandon science altogether.

A similar example often occurs in climate change debates. I frequently encounter people who admit that we are probably causing the climate to change, but they argue that we will never be able to curtail our greenhouse gas emissions in time to truly stop climate change, therefore we shouldn’t bother to do anything. Once again, however, an imperfect solution is better than no solution. Yes, we probably won’t be able to fully prevent climate change, but we can prevent the worst consequences of it (Schleussner et al. 2016), and that makes it worth taking action.

Another version of this fallacy also occurs during climate change debates, and it can be summarized as, “but not everyone else will do it.” For example, I often talk to Americans who argue that America should not try to limit its fossil fuel use because even if America switched to renewable energy sources, many other countries wouldn’t, so there is no point. Once again, the problem is that even if America was the only country to take climate change seriously (which is currently almost backwards of reality), that would still have an impact on the amount of warming that occurs. Further, the actions of others have no bearing on your own responsibility. In other words, the fact that everyone else is doing something unethical does not mean that it is ok for you to do it (yes, I know that was a philosophical argument not a scientific one, but I think that it is relevant here).

stick figure meme blue logical fallacy GMO golden rice

An example of the nirvana fallacy

A final variant of the nirvana fallacy occurs when you argue that a solution should not be used because it does not address some underlying issue. A good example of this comes from GMOs. Despite all of the anti-GMO propaganda, not all GMOs are about money. Some, such as golden rice and GMO bananas, are being developed solely as humanitarian endeavors. These GMOs are rich in vitamin A, which is currently lacking in the diets of some developing countries. Anti-GMO activists often respond to this by saying that the vitamin deficiency in developing countries is actually just a symptom of poverty, and those deficiencies would go away if we took care of economic inequality and food distribution. Really think about that response for a minute. They are actually arguing that all that we have to do to fix the problem is solve world hunger and poverty. Sure, fixing those things would solve the vitamin problem, and we should be trying to fix those problems, but we clearly aren’t going to find the solution any time soon. In contrast, we could be using vitamin rich GMOs within a few years or even months. Asking people who are suffering and even dying from vitamin deficiencies to wait for us to fix world hunger rather than using a GMO is absurd. In other words, it is true that the GMOs don’t address the underlying issue, but the underlying issue is a nearly impossible problem to solve. Therefore, we should use the solution that is available to us, even though it’s not perfect.

In short, an imperfect solution is generally better than not having any solution at all. Therefore, it is generally not valid to argue that a solution should not be used simply because it is imperfect or incomplete, unless you can provide a feasible and superior alternative.

Literature cited
Schleussner et al. 2016. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5C and 2C. Earth Syst. Dynam. 7:327-351.

Posted in Global Warming, GMO, Rules of Logic, Vaccines/Alternative Medicine | Tagged , , , , , , | 13 Comments

Understanding grants in science: doing research without selling your soul

Posted on June 14, 2016 by Fallacy Man

Last week was a good week for me, because I received a several thousand dollar grant for my research, so I wanted to take a few minutes to talk about exactly what that means. Many people seem to be under the impression that I can now go buy a new car, or that I have been bought off by some company and am now their pawn. In reality, all that it means is that I can do a research project that I wouldn’t have been able to do otherwise. So in this post, I want to clear up some myths and discuss how grants work in science.

Note: Throughout this post I am going to refer to anyone who provides a grant as a “grant agency” regardless of whether they are a company, society, government, charity, etc. Also, please note that I am speaking in very general terms. There are tons of different types of grants out there, so it is always possible to find unusual ones or exceptions the comments that I am going to make, but you should not make the mistake of focusing on the outliers and ignoring the general trends. In other words, I am not saying that there are no biased or unethical sources of funding, but most grants don’t work the way that many people seem to think they do.

You don’t get to keep the money
Addendum (14-June-16): Based on the comments, it seems that in some countries, such as the US, it is more common for grants to cover salaries than my personal experience with grants (which is largely not in the US) had lead me to believe. Please see the comments.

The first misconception that I want to clear up is the idea that scientists get to keep their grant money for their own expenditures. In a great many cases, a scientist’s salary is covered by the university, institution, or company that they work for. In those cases, all of the money goes into your research and you don’t get to keep a single cent (many grants explicitly state that they won’t cover salaries). Indeed, when you apply for the grant, you have to provide a detailed budget showing how you are going to spend the money, and at the end of the project, you are generally required to provide reports showing what you did, including providing evidence that you met the research objectives that you proposed in your application.

To be clear, there are some situations where a scientist’s salary is not covered by their university, and in those cases, you do apply for grants that include your salary, but even then, the salary can only be a very modest part of your budget. Trying to give yourself a high salary is pretty much guaranteed to result in your application getting tossed into the “do not fund” pile.

Now, you may be thinking, “well can’t you just lie and spend the money on personal stuff without telling anyone?” No, not really. First, grant money is usually locked up in institutional accounts. It doesn’t generally go into your personal account. This means that to use the money, you have to fill out purchase orders that go through your university. So there is accountability, and if you start trying to buy a bunch of clearly personal stuff, you’re going to get called on it. Second, you have to report back to the grant agency and show that you produced results. So if you spend the money on a vacation to Hawaii, you won’t have enough to do the project, which means you won’t produce results, which means that you’ll have a bad track record and won’t be able to get grants in the future. One of the things that grant committees want to see is evidence that you have spent previous grants wisely and produce high quality results. So blowing the grant on personal purchases is a really bad idea.

Note: When I say “produce results” I simply mean that you carried your project through to it’s conclusion and produced a report, paper, etc. I do not mean, “produced a specific result that the grant agency wanted.”

What’s the incentive?
At this point, you may be wondering what the incentive is for getting grants. If scientists don’t get to keep the money, then what’s the point? Indeed, by getting my recent grant, I have just committed myself to a project that will consume several months of my time and increase my pay by exactly 0 dollars. So why am I excited by it?

There are several answers to that question. First, a scientist’s status is determined largely by both the number and quality of papers that he/she produces. So getting a grant is a good thing because it lets you do research, which lets you publish, which lets you build your reputation as a scientist. For well established scientists, however, an extra paper doesn’t make that big of a difference. To be clear, it’s still important. Even if you have tenure you are usually expected to publish at least occasionally, but most scientists are workaholics, and working 60+ hours a week is extremely common. So why would we kill ourselves over these projects that produce little in the way of tangible personal benefits?

The answer is the underlying reason why many of us got into science in the first place: we love asking and answering questions. Becoming a scientist is an extremely long, difficult, and expensive process, so unless you really love doing research, you probably aren’t going to stick with it all the way through the PhD. As a result, those who do make it through tend to be extremely passionate about their work. So scientists get excited when we get grants because grants let us do the really cool research projects that we want to do. As a scientist, you get to pick what you want to study. So you write grants for projects that you are interested in and care about. In my case, not only do I get to answer an interesting question, but it is a question with strong implications for the conservation of many species of amphibians, and as a herpetologist/conservation biologist, that is something that I care greatly about. So I am excited by this grant because it well let me do research that I think is interesting and important.

To be clear, I’m not suggesting that all scientists are driven entirely by their love of research. We’re human. We have the same faults as everyone else, and there are always exceptions to the norm, but most of the people who stick with science do so because they love it (it’s certainly not for the money, because we make diddlysquat). Go to a conference sometime and talk to the scientists about their work. I think that you might be surprised by how deeply they care about what they are doing.

Grant agencies don’t control your results
Perhaps the most common myth that I encounter about grants is the idea that granting agencies control the results of the projects that they fund. I constantly hear non-scientists assert that grants, “come with strings attached,” and people often seem to think that when you get a grant, you agree to produce a particular result.  In reality, in the majority of cases, grant agencies have absolutely no control whatsoever over your results.  You tell them what you are going to test, not what result you are going to find. Also, at the end of your project, when you write your paper, you don’t have to submit it to them for approval before sending it to a journal. You do have to give them a written report of what you did, but they usually have no control over what you do with those data. In fact, in most cases, you can submit your paper for review before even informing them of your results. Just to give one comical example of this, an anti-vaccine group recently became very upset when scientists that they had funded published a paper showing a lack of evidence that vaccines cause autism.

Now, you may be thinking, “fine, they can’t stop you from publishing, but if you publish a result that they don’t like, you won’t get funding from them again.” First, that argument would only apply to the subset of grants that come from corporations or biased special interest groups (more on that in the next section). Second, within that subset, yes, using a  Merck grant to publish a paper showing that one of Merck’s products is dangerous might prevent you from getting a grant from them again, but what is your incentive for getting another grant from them? Remember, in many situations, you don’t get to keep any of the funding. You don’t benefit financially from it. Your only benefits are personal satisfaction and the increase in your scientific reputation, but spending months working 60 hours a week on a project, then scrapping all of that work and falsifying the results is the exact opposite of personal satisfaction. Similarly, writing a crappy paper with false results is a good way to ruin your scientific reputation. So risking your career by falsifying results just so that you can get another grant for a project that you may also have to falsify is downright stupid.

To be 100% clear, within the medical literature, there are very real and serious biases that need to be dealt with. It is true that research that is sponsored by pharmaceutical companies is more likely to favor those companies, but there are several things to note there. First, pharmaceutical companies tend to either fund their own research or use contract research organizations rather than giving out grants to academics. In those situations, the scientists often do get their salaries from the companies, and the companies do have a very large say in writing the papers. So those situations are fundamentally different from what I am talking about in this post. Also, I do not mean to suggest that scientists are in no way biased. Of course we have biases, and receiving a large grant certainly can give you a subconscious bias towards the company that gave you the grant. That is, however, not at all the same as the type of deliberate collusion that many people assume takes place.  

Grants aren’t given out by politicians
This is a somewhat bizarre myth, but I run into it frequently. Many people seem to be under the impression that politicians control the entire granting process and they get to decide who does and does not get the funding. In reality, there are tons of different grants out there, many of which have no connections to governments. Further, even when the grants are from governments, the politicians generally have very little to do with who gets them. They may set general limits (like X amount of money is for human health research), but they don’t decide the actual people/projects that get the money. That decision is usually made by a panel of scientists (often independent scientists).

You can do research that disagrees with big companies
The final myth that I want to address is the notion that you will never get funding for a topic that opposes large corporations. Again, there are lots of grants that aren’t associated with companies, and corporations do not control the entire grant process. So this notion is quite ridiculous.

Other people modify this claim to make it more general and argue that you have to study whatever the grant agency wants you to, but that is backwards of how things usually work. You decide what interests you, then you write a proposal for that topic and send it to a grant agency that you think might be interested in it. If they like it and don’t have any better proposals, you’ll get the money. If they don’t like it, you won’t. They don’t tell you what to study, rather you tell them what you are going to study.

To be clear, getting grants is not easy, and you usually have to write lots of applications before you actually get one (my project received several rejections before a grant agency finally accepted it). Also, you often have to try to sell your project differently depending on where you are applying, but at the end of the day, the project is yours. For example, my project is a genomics project on the conservation of rainforest frogs. Some of the grants that I applied for were very conservation focused, so for those applications, I stressed the conservation implications of my work. Others were really interested in genomics research, so for those I stressed that aspect. Still others were concerned about rainforests, so for those I emphasized my study subjects’ role as members of the rainforest community. So each application that I wrote was tailored to the interests of the people that I was applying to, but it was the same project on each application, I just presented it differently. Also, it is worth mentioning that grant agencies do sometimes recommend that you change your methodologies if they think that what you proposed was inadequate, but those recommendations usually come from the scientists who reviewed the grant proposals, not from the actual source of the grant.

So yes, getting grants is difficult, and if you want to get one, your project is going to need to have a solid scientific basis. If you’re writing proposals for flat-earth research, you’re not going to get any money. Similarly, if you are working on a very boring topic with no real-world applications, you’re going to have trouble getting money, but if you have an interesting question with good scientific support behind it, you can get money somewhere. This is especially true if your research has implications for human health. If you actually had a compelling reason to think that a particular vaccine was dangerous, for example, you could absolutely get funding for it. If you don’t believe me, just look at the hundreds of papers on vaccine safety, many of which were not funded by pharmaceutical companies. The dozens of studies on vaccines and autism, for example, exist because scientists took the concern over vaccines causing autism seriously and invested heavily in studying it. The fact that there was a potential that the research would be negative for pharmaceutical companies did not prevent the research from going forward (again, just to be clear, many of those studies were not funded by “big pharma” so you can’t accuse them all of being corrupt without committing an ad hoc fallacy).

Finally, just in case you aren’t convinced that corporations don’t control the process, think about the thousands of papers that have been published on humans causing climate change. If large companies were actually capable of preventing important research from moving forward, then surely multi-billion dollar oil companies could have prevented those climatologists from getting money.

via PhD Comics

via PhD Comics

Biases
I don’t want to paint an overly rosy picture of grants. There are plenty of real problems with the system, and one of the biggest (in my opinion) is the biased nature of grants. Some topics are much easier to get money for than others. For example, within conservation biology, the vast majority of money goes to “cute and cuddly” animals. If you want to do conservation research on pandas, tigers, etc. there’s plenty of money for that, but if you want to study rattlesnakes, snails, lizards, etc., good luck and may the force be with you. For example, a recent review of the literature on Australian mammals found that 73% of the papers were on marsupials (kangaroos, wallabies, koalas, etc.) despite the fact that Australia is home to a great many bats and rodents. Getting funding for those “ugly” animals is much more difficult than getting funding for “cute” charismatic animals.

There are similar biases in the medical literature. Some diseases get way more attention than others even though the neglected diseases are often more important. For example, there is a mismatch between the burden created by different types of cancer and the amount that we spend studying those cancers.

Another huge and regrettable bias is the bias against replication studies. Grant agencies like to see novel research that is tackling new questions, but replication is (or at least should be) the cornerstone of science. Nevertheless, if you want to do exactly what someone else has already done, you are going to have a really hard time getting funding.

Summary
In short, there are very real problems with the way that grants work. There are enormous biases in their distribution, and some topics are very hard to get funding for. Also, conflicts of interest do sometimes exist and should be taken seriously; however, the way that many opponents of science characterize grants is completely false. In many situations, scientists do not get to keep any of the grant money for themselves, and grant agencies usually have no control over the final results of the project. Similarly, grant agencies do not tell you what to study, rather you tell them what you want to study. So this notion that you can’t get funding for a project that might negatively impact a large company is absurd. There are lots of grants out there and many of them are not affiliated with companies. Finally, many of the biggest grants are from governments, but those grants are usually distributed by panels of expert scientists, not politicians.

Posted in Nature of Science | Tagged , | 8 Comments