We saw that 1) many metrics are stochastic, 2) what is stochastic can be hacked. This is the simplification of my work showing that "p-values are not p-values", i.e. highly sample dependent, with a skewed distribution. For instance for a "true" P value of .11, 53% of observations will show less than...

We saw that 1) many metrics are stochastic, 2) what is stochastic can be hacked. This is the simplification of my work showing that "p-values are not p-values", i.e. highly sample dependent, with a skewed distribution. For instance for a "true" P value of .11, 53% of observations will show less than .05. This allows for hacking: in a few trials a researcher can get a fake p-value of .01.
Paper is here and in Chapter 19 of SCOFT (Statistical Conseq of Fat Tails):

https://arxiv.org/pdf/1603.07532.pdf

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