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A statistical test applied to data that cannot support it produces results that appear sound but support little. This article introduces descriptive and inferential statistics, defines the common descriptive measures using a worked example, and describes t-tests, analysis of variance, chi-square tests, correlation and regression. Extracts from doctoral studies show how researchers report descriptive and inferential results and justify the use of non-parametric tests.

The article also explains how the type and shape of the data and the research questions together determine the choice of method, and summarizes that choice in a table and a decision flowchart. It is suited to research-methods and introductory statistics courses at master's and doctoral level. The article is the formal edition of a piece first published by Grad Coach and is released under CC BY 4.0.

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