In this episode we’ll talk about Null Hypothesis Significance Testing (or NHST) which is a framework for comparing two sets of information.

Today we’re going to discuss some problems with the logic of p-values, how they are commonly misinterpreted, how p-values don’t give us exactly what we want to know, and how that cutoff is arbitrary – and arguably not stringent enough in some scenarios.

We’re going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors.

Today, we’ll introduce some examples using both t-tests and z-tests and explain how critical values and p-values are different ways of telling us the same information.