方法证据记录
Bayesian Confirmatory Research
Bayesian confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Confirmatory Research Design
分类方法记录 · process-pipeline / research-design
- Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. · DOI 10.3758/PBR.16.2.225
- Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, A. J., Love, J., Selker, R., Gronau, Q. F., Smira, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57. · DOI 10.3758/s13423-017-1343-3
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