Anti-vaccers are fundamentally wrong about placebo-controlled trials | The Logic of Science
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Anti-vaccers are fundamentally wrong about placebo-controlled trials
Posted on July 6, 2026 by Fallacy Man
Anti-vaxxers love to demand placebo-controlled trials and insist that nothing else will suffice for demonstrating vaccine safety. This approach to medical research is flawed for a number of reasons. First, as I’ve explained previously, new vaccines are, in fact, tested against placebos. Truly novel vaccines (e.g., COVID vaccines) get tested against an inert, saline placebo (e.g., Polack et al. 2020), while updates to existing vaccines typically get tested against the previous versions of the vaccine, because those versions already have known safety and effectiveness profiles.
There is, however, a more fundamental issue with this insistence on placebo-controlled trials. Namely, they are not actually the best tool for examining vaccine safety, and cohort and case-control trials are often more useful. I’ve talked about this a number of times before (here, here, and here), but in this post, I want to try a more visual approach and actually show some data to illustrate the point.
As a brief thesis statement, correlation doesn’t automatically equal causation, but a lack of correlation does suggest a lack of causation (see note at end). Therefore, you do not need a placebo-controlled trial to make a statement like "vaccines do not cause autism" (within the statistical limits described below), and other study designs like cohort studies and case-control studies are actually better.
Note: we usually use “correlated” for continuous variables that change together (regressions) and “associated” for discrete variables, but for simplicity, I will just use “correlated” throughout.
Types of studies
There are three types of studies that are most relevant to this post (more details are here).
The first is the classic randomized controlled trial (aka RCT). In medical research, these are usually also placebo-controlled. This design takes a group of subjects, randomizes them into a treatment group (i.e., the group that receives a medicine) and a control group that receives a placebo instead of medicine. Some outcome of interest (e.g., a side effect) is then recorded in each group, and the rates of that outcome are compared.
Next, we have cohort studies. These use the same basic design as randomized placebo-controlled studies, but they are not manipulative. So rather than randomizing people into groups, it simply looks at groups who did and did not take a treatment (often via medical records) then compares the rates of the outcome of interest.
Finally, case-control studies work backwards. They identify a group of people with the outcome of interest (the “cases”) then match them with a group of people who are similar demographically but don’t have the outcome of interest (the “controls”). Then, the rates of a potential cause are compared between the groups.
Why randomize?
At the outset, we need to briefly discuss why randomized controlled trials (which, in medicine, are typically placebo-controlled) are generally so useful, as well as the nature of causation (note that the explanation below is simplified for the sake of brevity).
When we say that two things are correlated, we mean that they change together. So, there is a relationship between the two variables. The problem is that simply being related doesn’t mean that one thing is causing the other. For example, they could both be being caused by a third factor.
To use a famous example, ice cream sales and drowning are positively correlated (they both increase and decrease together). That does not, however, mean that ice cream causes drowning. Rather, a third factor (high temperatures) causes people to buy more ice cream and spend more time swimming (which results in more drowning accidents).
When we are trying to test for causation scientifically, we need to be extremely confident that there is no third factor driving the relationship. This is where randomization comes in. By randomly dividing people into two groups, we spread out all additional factors between the two groups so that no third factor can drive the results. This is the reason why randomized trials can confidently assert causation, while cohort and case-control trials cannot. For those designs, we can do our best to account for confounding factors (third factors) in the models, but it is always possible that we missed something. We just don’t have the confidence that we do with randomization.
So, if you want to say that X causes Y, you need a randomized trial. However, you do not need a randomized trial to say that X does not cause Y.
If you think about this for a second, the reason should be...