[YouTube] P-Value Hacking

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): Link to paper – A Short Note on P-Value Hacking

[YouTube] Breaking down Intuitively the Concept of Standard Deviation

SIMPLIFIED TUTORIAL, 1 (in the series where we break down concepts intuitively):
Before we talk about correlation, let’s discuss standard deviation, its analog in dimension 1. People don’t get while using it as a metric for deviation!

See the whole book (gets technical beyond Chapter 5)

Link to the Book: Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications