How Do I Know What My Theory Predicts?

2019/11/14 by Zoltán Dienes · 1 citation
Mathematics · Computer Science · Decision Sciences · #Advanced Statistical Methods and Models #Bayesian Modeling and Causal Inference #Forecasting Techniques and Applications

paper · doi:10.1177/2515245919876960

openalex publication_date 2019/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

To get evidence for or against a theory relative to the null hypothesis, one needs to know what the theory predicts. The amount of evidence can then be quantified by a Bayes factor. Specifying the sizes of the effect one’s theory predicts may not come naturally, but I show some ways of thinking about the problem, some simple heuristics that are often useful when one has little relevant prior information. These heuristics include the room-to-move heuristic (for comparing mean differences), the ratio-of-scales heuristic (for regression slopes), the ratio-of-means heuristic (for regression slopes), the basic-effect heuristic (for analysis of variance effects), and the total-effect heuristic (for mediation analysis).

Citations

• Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences.
• Theory-Testing in Psychology and Physics: A Methodological Paradox
• Standardized or simple effect size: What should be reported?
• Optional stopping: No problem for Bayesians
• The philosophy of Bayes factors and the quantification of statistical evidence
• Is There a Free Lunch in Inference?
• Increasing Transparency Through a Multiverse Analysis
• Determining informative priors for cognitive models
• Invited Commentary: The Need for Cognitive Science in Methodology

Cited by

• KORELASI PANJANG TUNGKAI DAN DAYA LEDAK OTOTTUNGKAI TERHADAP JAUHNYA HASIL TENDANGANBOLA PADA SISWA EKSTRAKURIKULER SEPAK BOLASMA NEGERI 1 NGRAMBE KABUPATEN NGAWITAHUN 2008/2009
• Bayes factor [wikipedia]