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Significance Testing is a book category focused on the theory and practice of statistical hypothesis testing—how to frame null and alternative hypotheses, calculate test statistics and p-values, and make decisions under uncertainty. It includes introductory and advanced treatments of common procedures (t-tests, chi-square tests, ANOVA, nonparametric tests), power and sample-size analysis, multiple comparisons, sequential testing, and applications within generalized linear models. Many titles emphasize practical workflows for fields like biomedicine, psychology, economics, and product analytics (A/B testing), often with examples in R, Python, or SPSS. The category also features guidance on interpretation and reporting (effect sizes, confidence intervals), discussions of pitfalls and misuse (Type I/II errors, p-hacking, replication issues), and comparisons with Bayesian and estimation-focused alternatives. These books serve students, researchers, and practitioners who design studies, analyze data, and seek to draw reliable, transparent inferences from empirical evidence.

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