Multiverse Analysis

This book uses computational tools to improve the credibility and transparency of science.

In the crisis of science, there is growing scepticism about the reliability and objectivity of research. This book develops computational multiverse methods to make evidence more transparent, and shows how to evaluate the credibility and robustness of research.There are many ways of conducting an analysis, but most studies show only a few carefully curated estimates. Applied research involves a complex array of analytical decisions, often leading to a 'garden of forking paths' where each choice can lead to different results. By systematically exploring how alternative analytical choices affect the findings, Multiverse Analysis reveals the full range of estimates that the data can support and uncovers insights that single-path analyses often miss. It shows which modelling decisions are most critical to the results and reveals how data and assumptions work together to produce empirical estimates. Focusing on intuitive understanding rather than complex mathematics, and drawing on real-world datasets, this book provides a step-by-step guide to comprehensive multiverse analysis. Go beyond traditional, single-path methods and discover how multiverse analysis can lead to more transparent, illuminating, and persuasive empirical contributions to science.