This book provides a coherent description of foundational matters concerning statistical inference and shows how statistics can help us make inductive inferences about a broader context, based only on a limited dataset such as a random sample drawn from a larger population. By relating those basics to the methodological debate about inferential errors associated with
p-values and statistical significance testing, readers are provided with a clear grasp of what statistical inference presupposes, and what it can and cannot do. To facilitate intuition, the representations throughout the book are as non-technical as possible.
The central inspiration behind the text comes from the scientific debate about good statistical practices and the replication crisis. Calls for statistical reform include an unprecedented methodological warning from the
American Statistical Association in 2016, a special issue Statistical Inference in the 21st Century: A World Beyond
p < 0.05 of
The American Statistician in 2019, and a widely supported call to Retire statistical significance in
Nature in 2019.
The book elucidates the probabilistic foundations and the potential of sample-based inferences, including random data generation, effect size estimation, and the assessment of estimation uncertainty caused by random error. Based on a thorough understanding of those basics, it then describes the p-value concept and the null-hypothesis-significance-testing ritual, and finally points out the ensuing inferential errors. This provides readers with the competence to avoid ill-guided statistical routines and misinterpretations of statistical quantities in the future.
Intended for readers with an interest in understanding the role of statistical inference, the book provides a prudent assessment of the knowledge gain that can be obtained from a particular set of data under consideration of the uncertainty caused by random error. More particularly, it offers an accessible resource for graduate students as well as statistical practitioners who have a basic knowledge of statistics. Last but not least, it is aimed at scientists with a genuine methodological interest in the above-mentioned reform debate.
- 1. Introduction. - 2. The Meaning of Scientific and Statistical
Inference. - 3. The Basics of Statistical Inference: Simple Random Sampling.
- 4. Estimation Uncertainty in Complex Sampling Designs. - 5. Knowledge
Accumulation Through Meta-analysis and Replications. - 6. The p-Value and
Statistical Significance Testing. - 7. Statistical Inference in Experiments.
- 8. Better Inference in the 21st Century: A World Beyond p < 0.05.
Norbert Hirschauer is Professor of Agribusiness Management at the Martin Luther University Halle-Wittenberg, Germany. His research fields include whole-farm risk analysis, economics of crime and compliance, behavioral and experimental economics, and statistical inference. Since 2015, he has headed an informal working group that includes the books co-authors and concerns itself with inferential errors and the replication crisis in the social sciences.
Sven Grüner is a PostDoc in the Agribusiness Management Group of the Martin Luther University Halle-Wittenberg, Germany. His research focus lies in behavioral and experimental economics. Within this realm, he is interested in the external validity of behavioral study findings. He has been a member of the working group on inferential errors and the replication crisis since 2015.
Oliver Mußhoff is Professor of Farm Management at the Georg-August-University Göttingen, Germany. He has worked on a broadrange of research questions in the field of agricultural economics, including modeling of entrepreneurial decisions, investment and finance, risk management as well as experimental impact analysis of agricultural policy measures. He has been a member of the working group on inferential errors and the replication crisis since 2015.
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