Understanding Inferential Statistics

What actually is a p-value for? And what is a significant result? This book answers such questions by providing a compact introduction to inferential statistics. Our major focus is on the logic of inferential statistics and hypothesis testing: We cover the logic behind statistical tests, we then walk through the most common procedures (t-test, analysis of variance with and without repeated measures, correlation/regression) and discuss pitfalls of data analysis. The book thus helps developing a solid understanding of how common test procedures work, and how to interpret their results correctly. Hands-on examples from everyday research including exemplary calculations with the programs SPSS and R supplement each chapter. In addition to classical methods, we briefly discuss recent developments in psychological research methods.

This book is a translation of the original German 3rd edition of Inferenzstatistik Verstehen by Markus Janczyk and Roland Pfister. It is based on an AI-powered machine translation (by the service DeepL. com), with thorough follow-up editing by both authors.

Introduction and descriptive statistics. - Basics of inferential statistics 1: random variables. - Basics of inferential statistics 2: population and parameter estimation. - Hypothesis testing and significance. - Difference hypotheses with a maximum of two groups: t-tests. Confidence intervals. - Error types, effect size and power. - Single factor analysis of variance. - Multifactorial analysis of variance. - Analysis of variance with repeated measures. - Correlation hypotheses: Correlation and regression. - Concluding remarks.