Fundamentals of Statistical Inference

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 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 book's 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 broad range 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.

Contenu

• 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.