Bayesian inference for psychology. Part I : Theoretical advantages and practical ramifications

• Autoři: WAGENMAKERS Eric-Jan; MARSMAN Marteen; JAMIL Tahira; LY Alexander; VERHAGEN Josine; LOVE Jonathon; SELKER Ravi; GRONAU Quentin F.; ŠMÍRA Martin; EPSKAMP Sacha; MATZKE Dora; ROUDER Jeffrey N.; MOREY Richard D.
• Rok publikování: 2018
• Druh: Článek v odborném periodiku
• Časopis / Zdroj: Psychonomic Bulletin & Review
• Fakulta / Pracoviště MU: Fakulta sociálních studií
• www: https://doi.org/10.3758/s13423-017-1343-3
• Doi: http://dx.doi.org/10.3758/s13423-017-1343-3
• Obor: Psychologie
• Klíčová slova: Hypothesis test; Statistical evidence; Bayes factor; Posterior distribution

Přiložené soubory

• BayesianInferenceForPsychologyI.pdf

• Popis: Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).