Quantopian Lecture Series: p-Hacking and Multiple Comparisons Bias

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Multiple comparisons bias is a big problem in quantitative analysis. Effectively it's just the notion that the more tests you run, the more likely you are to get false positives (things that look like they confirm your hypothesis, but are really just random chance). If you don't correct for this at some point you're very likely to accept hypotheses that aren't based on any real relationships. p-Hacking is just the abuse of this phenomenon, in which someone runs tons of tests until they find one specific situation in which their tests pass. It can be intentional or inadvertent, but it happens. This lecture will introduce you to the concept, show some experimental examples, and talk about correcting for it.

All of our lectures can be found here:
www.quantopian.com/lectures.

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3 responses

Delaney Mackenzie

p-Hacking can also be explained as follows:

"All lies and jests Still a man hears what he wants to hear
And disregards the rest"
- Simon and Garfunkel, 1969

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Burrito Dan

Bonferroni is overly conservative as noted in the lecture, but Holm-Bonferroni is a good next step, and has an obvious application to pipeline. If we are doing a statistical test on all stocks in our universe, what is the significance level we should use to say an individual stock is above a cut off. Holm-Bonferroni suggests ranking and a formula for the cut-off. Does this make sense?

https://en.m.wikipedia.org/wiki/Holm–Bonferroni_method

Maxwell Margenot

This seems interesting! It definitely looks like a good improvement on the original Bonferroni correction (and I like that there's a proof on the Wikipedia page). An overall more powerful test is compelling.

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