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In spite of the immense amount of fruitful labour which has been expended in its practical applications, the basic principles of this organ of science are still in a state of obscurity, and it cannot be denied that, during the recent rapid development of practical methods, fundamental problems have been ignored and fundamental paradoxes left unresolved.

(Fisher, 1922a, p. 310)

Our statistics review includes topics that would customarily be seen in a first course in statistics at the undergraduate level, but depending on the given course and what was emphasized by the instructor, our treatment here may be at a slightly deeper level. We review these principles with demonstrations in R and SPSS where appropriate. Should any of the following material come across as entirely “new,” then a review of any introductory statistics text is recommended. For instance, Kirk (2008), Moore, McCabe, and Craig (2014), Box, Hunter, and Hunter (1978) are relatively nontechnical sources, whereas Degroot and Schervish (2002), Wackerly, Mendenhall III, and Scheaffer (2002) along with Evans and Rosenthal (2010) are much deeper and technically dense. Casella and Berger (2002), Hogg and Craig (1995) along with Shao (2003) are much higher‐level theoretically oriented texts targeted mainly at mathematical and theoretical statisticians. Other sources include Panik (2005), Berry and Lindgren (1996), and Rice (2006). For a lighter narrative on the role of statistics in social science, consult Abelson (1995).

Because of its importance in the interpretation of evidence, we close the chapter with an easy but powerful demonstration of what makes a p‐value small or large in the context of statistical significance testing and the testing of null hypotheses. It is imperative that as a research scientist, you are knowledgeable of this material before you attempt to evaluate any research findings that employ statistical inference.