Reviewing Inferential Statistics

An open textbook that takes a holistic approach to probability and inferential statistics. It covers the central limit theorem, confidence intervals, hypothesis testing, chi-square tests, ANOVA, and regression, with worked examples throughout.

Maurice A. Geraghty

A clear introductory unit on inferential statistics. It explains estimation and hypothesis testing, including types of hypotheses, significance levels, one and two-tailed tests, and the errors involved in testing.

eGyanKosh

A concise review that introduces inferential statistics for research practice. It explains p values, confidence intervals, and parametric and non-parametric tests, and includes a practical checklist for applying them to a dataset.

Gill Marshall and Leon Jonker

A short, hands-on guide to the ideas behind statistical inference, from sampling distributions to confidence intervals and hypothesis tests. It uses simulations in Stata to make the abstract concepts concrete.

Diego Farren

A readable chapter that lays out the logic of inferential testing step by step, from selecting a representative sample to computing a test statistic and interpreting the result.

Simon Fraser University