Statistics by Simulation A Synthetic Data Approach
Statistics by Simulation A Synthetic Data Approach by Carsten F. Dormann, published by Princeton University Press on June 3, 2025, is a comprehensive guide designed to enhance understanding of statistics through the use of simulations. This 456-page textbook addresses real-world challenges such as small sample sizes and skewed data distributions, presenting data simulations as a valuable tool for validating statistical reasoning and improving study design. The book aims to make the concept of data simulation accessible to a wider audience, featuring examples from various scientific disciplines.
Readers will find a structured approach to statistical practice, covering all essential steps from project planning to post-hoc analysis and model checking. The text includes practical examples from fields such as sociology, psychology, and economics, along with R code for each example, which is available online. The book minimizes jargon and is suitable for those with a basic statistical background, making it a practical resource for researchers and students looking to enhance their analytical workflows and develop new statistical methods.
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An accessible guide to understanding statistics using simulations, with examples from a range of scientific disciplines
Real-world challenges such as small sample sizes, skewed distributions of data, biased sampling designs, and more predictors than data points are pushing the limits of classical statistical analysis. This textbook provides a new tool for the statistical toolkit: data simulations. It shows that using simulation and data-generating models is an excellent way to validate statistical reasoning and to augment study design and statistical analysis with planning and visualization. Although data simulations are not new to professional statisticians, Statistics by Simulation makes the approach accessible to a broader audience, with examples from many fields. It introduces the reasoning behind data simulation and then shows how to apply it in planning experiments or observational studies, developing analytical workflows, deploying model diagnostics, and developing new indices and statistical methods.
• Covers all steps of statistical practice, from planning projects to post-hoc analysis and model checking
• Provides examples from disciplines including sociology, psychology, ecology, economics, physics, and medicine
• Includes R code for all examples, with data and code freely available online
• Offers bullet-point outlines and summaries of each chapter
• Minimizes the use of jargon and requires only basic statistical background and skills
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