Chapter 8: Common Statistical Significance Tests

The basic idea of common statistical tests in the approach we have taken has been the following:

Observe some data
Construct a model of the data, with a parameter that needs to be estimated, such as the “true” single value ( $\mu$ , in Section 7.3), or the proportion of the event ( $\theta$ , in Section 7.4).
Calculate the final, posterior probability of that parameter
“Test” to see if there is a significant (usually 95%) probability that the parameter is not zero.

Adapted from Statistical Inference for Everyone, by Brian Blais (Bryant University), licensed under CC BY-SA 4.0 (dual-licensed under the GNU FDL 1.2 or later; this adaptation uses the CC BY-SA grant). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.