Inferential statistics lets you draw conclusions about a whole population from a single sample. We gathered this collection of inferential statistics books in PDF so you can master that skill for free.
These titles walk you through the core of statistical reasoning. You will find hypothesis testing, confidence intervals, estimation, and regression explained step by step.
You get open textbooks from universities and recognized authors, covering both frequentist and Bayesian approaches. Download every inferential statistics book you need, with no cost and no sign-up.
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Foundations
Inferential Statistics Books
Start here with general introductions that explain how samples reveal the behavior of an entire population. These books on inferential statistics build the foundation you need before moving into deeper theory.
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.
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.
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.
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.
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.
A focused chapter that reviews the essentials of inferential statistics, including normal distributions, estimation, and the five steps of hypothesis testing. Worked examples and exercises reinforce each concept.
A comprehensive slide-based course on inferential statistics for research and health settings. It covers estimation, hypothesis testing, parametric and non-parametric methods, and the most common statistical tests.
These texts go deeper into the theory behind statistical inference, covering estimation and the logic of frequentist and Bayesian methods. They suit readers who want a rigorous, complete treatment of the subject.
An accessible textbook that teaches statistical inference from the ground up, using a clear Bayesian view of probability and evidence. Everyday examples, from coin flips to real datasets, build genuine understanding.
A thorough set of university lecture notes covering both descriptive and inferential statistics. It builds from probability and distributions to estimation, confidence intervals, hypothesis testing, correlation, and regression.
Lecture notes that introduce the formal framework of statistical inference. They explain parametric and non-parametric models, point estimation, and how to reason about an unknown population from sample data.
Practice the methods that matter most in research, including p-values, t-tests, and worked problem sets. These hypothesis testing books turn theory into skills you can apply right away.
A chapter packed with worked practice questions on inferential statistics. It drills the normal distribution, confidence intervals, and hypothesis testing through fully solved problems.
A compact set of worked problems on confidence intervals and inference, with complete solutions. Good practice for applying the central limit theorem to real questions.
A short set of study questions and multiple-choice problems on t-tests and point estimation. A handy self-check for the core ideas behind hypothesis testing.