Multiverse Analysis: Computational Methods for Robust Results - Rilegato

Young, Cristobal; Cumberworth, Erin

Rilegato

ISBN 10: 1316518787 ISBN 13: 9781316518786

Casa editrice: Cambridge Univ Pr, 2025

Riassunto

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.

Dettagli dell'edizione

• Editore: Cambridge Univ Pr
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1316518787
• ISBN 13: 9781316518786
• Rilegatura: Copertina rigida
• Numero di pagine: 200
• Contatto del produttore: Cambridge University Press; https://www.cambridge.org/; ; Via Honduras,15; Pomezia; Italia; 71; Italia