Afsnitsforfatter: Danielle J. Navarro and David R. Foxcroft

Summary

Null hypothesis testing is one of the most ubiquitous elements to statistical theory. The vast majority of scientific papers report the results of some hypothesis test or another. As a consequence it is almost impossible to get by in science without having at least a cursory understanding of what a p-value means, making this one of the most important chapters in the book. As usual, I will end the chapter with a quick recapitulation of the key ideas that we have talked about:

• Research hypotheses and statistical hypotheses, Null and alternative hypotheses

• Type I and Type II errors

• Test statistics and sampling distributions

• Hypothesis testing as a decision making process

• *p*-values as “soft” decisions

• Writing up the results of a hypothesis test

• Running the hypothesis test in practice

• Effect size, sample size and power

• Some issues to consider regarding hypothesis testing

Later in the book, in chapter Bayesian statistics, I will revisit the theory of null hypothesis tests from a Bayesian perspective and introduce a number of new tools that you can use if you are not particularly fond of the orthodox approach. But for now, though, we are done with the abstract statistical theory, and we can start discussing specific data analysis tools.