Model-averaged Bayesian t tests

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00604111" target="_blank" >RIV/67985807:_____/25:00604111 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3758/s13423-024-02590-5" target="_blank" >https://doi.org/10.3758/s13423-024-02590-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3758/s13423-024-02590-5" target="_blank" >10.3758/s13423-024-02590-5</a>

Result language

angličtina

Original language name

Model-averaged Bayesian t tests

Original language description

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the RoBTT package in R. A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Psychonomic Bulletin & Review

ISSN

1069-9384

e-ISSN

1531-5320

Volume of the periodical

32

Issue of the periodical within the volume

3

Country of publishing house

DE - GERMANY

Number of pages

25

Pages from-to

1007-1031

UT code for WoS article

001349808300003

EID of the result in the Scopus database

2-s2.0-85208577677