The false dichotomy of multiple hypothesis testing

[Disclaimer: I’m not a statistician, but I do play one at work from time to time. If I’ve gotten something wrong here please point it out to me. This is an evolving thought process for me that’s part of the larger picture of what the scientific method does and doesn’t mean- not the definitive truth about multiple hypothesis testing.]

There’s a division in research between hypothesis-driven and discovery-driven endeavors. In hypothesis-driven research you start out with a model of what’s going on (this can be explicitly stated or just the amalgamation of what’s known about the system you’re studying) and then design an experiment to test that hypothesis (see my discussions on the scientific method here and here). In discovery-driven research you start out with more general questions (that can easily be stated as hypotheses, but often aren’t) and generate larger amounts of data, then search the data for relationships using statistical methods (or other discovery-based methods).

The problem with analysis of large amounts of data is that when you’re applying a statistical test to a dataset you are actually testing many, many hypotheses at once. This means that your level of surprise at finding something that you call significant (arbitrarily but traditionally a p-value of less than 0.05) may be inflated by the fact that you’re looking a whole bunch of times (thus increasing the odds that you’ll observe SOMETHING just on random chance alone- see this excellent xkcd cartoon for an example, see below since I’ll refer to this example). So you need to apply some kind of multiple hypothesis correction to your statistical results to reduce the chances that you’ll fool yourself into thinking that you’ve got something real when actually you’ve just got something random. In the XKCD example below a multiple hypothesis correction using Bonferroni’s method (one of the simplest and most conservative corrections) would suggest that the threshold for significance should be moved to 0.05/20=0.0025 – since 20 different tests were performed.

Here’s where the problem of a false dichotomy occurs. Many researchers who analyze large amounts of data believe that utilizing a hypothesis-based approach mitigates the effect of multiple hypothesis testing on their results. That is, they believe that they can focus their investigation of the data to a subset constrained by a model/hypothesis and thus reduce the effect that multiple hypothesis testing has on their analysis. Instead of looking at 10,000 proteins in a study they now look at only the 25 proteins that are thought to be present in a particular pathway of interest (where the pathway here represent the model based on existing knowledge). This is like saying, “we believe that jelly beans in the blue green color range cause acne” and then drawing your significance threshold at 0.05/4=0.0125 – since there are ~4 jelly beans tested that are in the blue-green color range (not sure if ‘lilac’ counts or not- that would make 5). All well and good EXCEPT for the fact that the actual chance of detecting something by random chance HASN’T changed. In large scale data analysis (transcriptome analysis, e.g.) you’ve still MEASURED everything else. You’ve just chosen to limit your investigation to a smaller subset and then can ‘go easy’ on your multiple hypothesis correction.

The counter-argument that might be made to this point is that by doing this you’re testing a specific hypothesis, one that you believe to be true and may be supported by existing data . This is a reasonable point in one sense- it may lend credence to your finding that there is existing information supporting your result. But on the other hand it doesn’t change the fact that you still could be finding more things by chance than you realize because you simply hadn’t looked at the rest of your data. It turns out that this is true not just of analysis of big data, but also of some kinds of traditional experiments aimed at testing individual – associative- hypotheses. The difference there is that it is technically unfeasible to actually test a large amount of the background cases (generally limited to one or two negative controls). Also a mechanistic hypothesis (as opposed to an associative one) is based on intervention, which tells you something different and so is not (as) subject to these considerations.

Imagine that you’ve dropped your car keys in the street and you don’t know what they look like (maybe borrowing a friend’s car). You’re pretty sure you dropped them in front of the coffee shop on a block with 7 other shops on it- but you did walk the length of the block before you noticed the keys were gone. You walk directly back to look in front of the coffee shop and find a set of keys. Great, you’re done. You found your keys, right? What if you looked in front of the other stores and found other sets of keys. You didn’t look- but that doesn’t make it less likely that you’re wrong about these keys (your existing knowledge/model/hypothesis “I dropped them in front of the coffee shop” could easily be wrong).

XKCD: significant

One thought on “The false dichotomy of multiple hypothesis testing

  1. Hi Jason,
    This is an interesting article and I appreciate your writing it up to share. I agree with alot of what you say and this had me thinking about how searching for p values or significance levels based on statistical distributions in hopes of learning “something good” from the data can be problematic. When I think about the approaches used to solve certain problems, I can certainly think of examples that are using approaches that are probably not actually going to tell us much we really want to know.
    For example, using statistical methods, regardless of how “sophisticated” to test differences of protein or gene expression that involve parametric assumptions of independence with expression levels that are almost guaranteed to be correlated, and considering these genes or proteins at the univariate level with statistical tests seems almost silly, but it is done all the time. It would be neat to see analysts more often use or develop truly multivariate methods to consider all variables at once in these scenarios. Also, using methods considering multivariate differences could possibly make significance testing only slightly more appropriate. Dependence on some p value would never be what I’d want to base my results on. Instead, in the type of scenarios you mention, exploring other conditions, perhaps how well a multivariate statistic characterizes the true separation of biological classes and then providing further evidence of this separation appropriately would be more convincing than some p value. Ensemble based models aggregating information over various partitions of the data instead of fishing for significance may be helpful. I don’t personally like corrections for multiple testing because this is still often using a flawed approach with data not meeting the assumptions of the tests and uses for univariate significance in scenarios where such tests may not be appropriate. I may be wrong, but these are my opinions.
    Thanks for provoking thought on this for me!

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