### Abstract

Original language | English |
---|---|

Pages (from-to) | 539-556 |

Journal | Psychological Methods |

Volume | 24 |

Issue number | 5 |

DOIs | |

Publication status | Published - 2019 |

### Fingerprint

### Keywords

- Bayes Factor
- Bayesian error probabilities
- CONSTRAINED HYPOTHESES
- INEQUALITY
- MODEL SELECTION
- NEYMAN
- P-VALUES
- PREVALENCE
- REPLICATION
- bain
- informative hypotheses
- posterior probabilities

### Cite this

*Psychological Methods*,

*24*(5), 539-556. https://doi.org/10.1037/met0000201

}

*Psychological Methods*, vol. 24, no. 5, pp. 539-556. https://doi.org/10.1037/met0000201

**A tutorial on testing hypotheses using the Bayes factor.** / Hoijtink, Herbert; Mulder, Joris; van Lissa, Caspar; Gu, Xin.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - A tutorial on testing hypotheses using the Bayes factor

AU - Hoijtink, Herbert

AU - Mulder, Joris

AU - van Lissa, Caspar

AU - Gu, Xin

PY - 2019

Y1 - 2019

N2 - Learning about hypothesis evaluation using the Bayes factor could enhance psychological research. In contrast to null-hypothesis significance testing it renders the evidence in favor of each of the hypotheses under consideration (it can be used to quantify support for the null-hypothesis) instead of a dichotomous reject/do-not-reject decision; it can straightforwardly be used for the evaluation of multiple hypotheses without having to bother about the proper manner to account for multiple testing; and it allows continuous reevaluation of hypotheses after additional data have been collected (Bayesian updating). This tutorial addresses researchers considering to evaluate their hypotheses by means of the Bayes factor. The focus is completely applied and each topic discussed is illustrated using Bayes factors for the evaluation of hypotheses in the context of an ANOVA model, obtained using the R package bain. Readers can execute all the analyses presented while reading this tutorial if they download bain and the R-codes used. It will be elaborated in a completely nontechnical manner: what the Bayes factor is, how it can be obtained, how Bayes factors should be interpreted, and what can be done with Bayes factors. After reading this tutorial and executing the associated code, researchers will be able to use their own data for the evaluation of hypotheses by means of the Bayes factor, not only in the context of ANOVA models, but also in the context of other statistical models.

AB - Learning about hypothesis evaluation using the Bayes factor could enhance psychological research. In contrast to null-hypothesis significance testing it renders the evidence in favor of each of the hypotheses under consideration (it can be used to quantify support for the null-hypothesis) instead of a dichotomous reject/do-not-reject decision; it can straightforwardly be used for the evaluation of multiple hypotheses without having to bother about the proper manner to account for multiple testing; and it allows continuous reevaluation of hypotheses after additional data have been collected (Bayesian updating). This tutorial addresses researchers considering to evaluate their hypotheses by means of the Bayes factor. The focus is completely applied and each topic discussed is illustrated using Bayes factors for the evaluation of hypotheses in the context of an ANOVA model, obtained using the R package bain. Readers can execute all the analyses presented while reading this tutorial if they download bain and the R-codes used. It will be elaborated in a completely nontechnical manner: what the Bayes factor is, how it can be obtained, how Bayes factors should be interpreted, and what can be done with Bayes factors. After reading this tutorial and executing the associated code, researchers will be able to use their own data for the evaluation of hypotheses by means of the Bayes factor, not only in the context of ANOVA models, but also in the context of other statistical models.

KW - Bayes Factor

KW - Bayesian error probabilities

KW - CONSTRAINED HYPOTHESES

KW - INEQUALITY

KW - MODEL SELECTION

KW - NEYMAN

KW - P-VALUES

KW - PREVALENCE

KW - REPLICATION

KW - bain

KW - informative hypotheses

KW - posterior probabilities

U2 - 10.1037/met0000201

DO - 10.1037/met0000201

M3 - Article

VL - 24

SP - 539

EP - 556

JO - Psychological Methods

JF - Psychological Methods

SN - 1082-989X

IS - 5

ER -