Explorations in statistics: hypothesis tests and P values.
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.
Curran-Everett
Douglas
D
National Jewish Health, Department of Biostatistics and Informatics, University of Colorado Denver, USA. EverettD@NJHealth.org
D000465:Algorithms; D012107:Research Design / Q000706:statistics & numerical data / Q000639:trends; D013223:Statistics as Topic / Q000379:methods* / Q000639:trends*