p-Hacking and False Discovery in A/B Testing

If you’ve taken some statistics or econometrics, you’ve probably heard of “significance levels” and “p-values”. For some reason, academia choose 0.05 as a threshold for “meaningful” or “significant” results.

Generally, a 0.05 p-value means that you would observe your result in 5% of experiments due to random sampling error. I.e. if I tested “is X correlated with cancer”, and my null hypothesis is “X isn’t correlated with cancer”, a 0.05 p-value would meet the threshold to reject that null hypothesis. Generally, a lower p-value means a more statistically significant result.

The problem is that 0.05 seems to be much too high of a p-value. I.e. clever experimental design and cherry picking can generate many results that are statistically significant at that level. Many academics advocate for moving to a 0.01 or even 0.001 significance threshold.

Recently, in some academic fields, there’s been widespread concern that many research studies were p-hacked. See for example, this paper that blew up last year in the finance community, because it suggests a significant number of finance papers, including some seminal ones, had p-hacked results: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3017677.

The counter-argument is that for certain scientific fields, you may never be able to reach a p-value threshold of 0.001. This means the vast majority of research couldn’t be published in journals, academics wouldn’t be able to get promoted etc.

Even better than examples and explanations, learn by doing: https://projects.fivethirtyeight.com/p-hacking/

"You’re a social scientist with a hunch: The U.S. economy is affected by whether Republicans or Democrats are in office. Try to show that a connection exists, using real data going back to 1948. For your results to be publishable in an academic journal, you’ll need to prove that they are “statistically significant” by achieving a low enough p-value."

This is wrong. P hacking has nothing to do with the p being too lenient.

This. I can p-hack in my field, if I wanted to, up to a p-value of arbitrary strictness, given enough time.

I would be shocked if it were as low as 57%. As an intern, I found that the analysts in charge of A/B tests often didn't have a background in science or running experiments, and didn't really care. There were a couple of data analytics teams in the company, and I think a lot of the developers didn't like my team because we were seen as "fussier" than the other one. We required people to preregister hypotheses, and run experiments for predetermined amounts of time.

I don't think the tech environment is very conducive to running experiments. Everything moves too fast, by the time you figure out the results someone gives you are bs, they've already got promoted 3 times and work as a director at a different company.

I work in science now, and although people still p-hack like hell, there's at least some sort of shame about it. There's a long term cost too, I've met a couple researchers who have spent years trying to replicate some finding they got early in their career through suspicious means.