Textbook in PDF format

This fully updated fourth edition explores the foundations of statistical reasoning, focusing on how to interpret psychological data and statistical results. This edition includes three important new features. First, the book is closely integrated with the free statistical analysis program JASP. Thus, students learn how to use JASP to help with tasks such as constructing grouped frequency distributions, making violin plots, conducting inferential statistical tests, and creating confidence intervals. Second, reflecting the growing use of Bayesian analyses in the professional literature, this edition includes a chapter with an introduction to Bayesian statistics (also using JASP). Third, the revised text incorporates adjunct questions, that is, questions that challenge the student’s understanding, after each major section. Cognitive psychology has demonstrated how adjunct questions and related techniques such as self-explanation can greatly improve comprehension.
Additional key features of the book include:
A user-friendly approach, with focused attention to explaining the more difficult concepts and the logic behind them. End of chapter tables summarize the hypothesis testing procedures introduced, and exercises support information recall and application.
The consistent use of a six-step procedure for all hypothesis tests that captures the logic of statistical inference.
Multiple examples of each of the major inferential statistical tests.
Boxed media reports illustrate key concepts and their relevance to real-world issues.
A focus on power, with a separate chapter, and power analysis procedures in each chapter.
With comprehensive digital resources, including large data sets integrated throughout the textbook, and files for conducting analysis in JASP, this is an essential text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences.
Preface
Why statistics?
Frequency distributions and percentiles
Central tendency and variability
z scores and normal distributions
Overview of inferential statistics
Probability
Sampling distributions
Logic of hypothesis testing
Power
Logic of parameter estimation
Inferences about population proportions using the z statistic
Inferences about μ when σ is unknown: The single-sample t test
Comparing two population means: Independent samples
Random sampling, random assignment, and causality
Comparing two populations: Dependent samples
Comparing more than two population means: Independent samples
One-factor ANOVA for dependent samples
Introduction to factorial designs
Describing linear relationships: Regression
Measuring the strength of linear relationships: Correlation
Inferences from nominal data: The χ2 statistic
Introduction to Bayesian statistics
Glossary of symbols
Tables
Appendix A
Appendix B
Answers to selected exercises
Index