The DOI matches its Crossref record; 199 of the 199 references it cites were found in a registry or an index.
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Open accessAbstract
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).
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Cited by
Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019) · Journal of Open Source Software · doi:10.21105/joss.01541 · Cited by 1,483
The JASP guidelines for conducting and reporting a Bayesian analysis
van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., et al. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01798-5 · Cited by 1,114
Self-citationIndices of Effect Existence and Significance in the Bayesian Framework
Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., & Lüdecke, D. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02767 · Cited by 983
Addressing the theory crisis in psychology
Oberauer, K., & Lewandowsky, S. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-019-01645-2 · Cited by 462
A Conceptual Introduction to Bayesian Model Averaging
Hinne, M., Gronau, Q. F., van den Bergh, D., & Wagenmakers, E.-J. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245919898657 · Cited by 318
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Metadata
- Authors
- Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D.
- Venue
- Psychonomic Bulletin & Review
- Published
- 2017-08-04
- Volume
- 25
- Issue
- 1
- Pages
- 35-57
- ISSN
- 1069-9384 (ISSN-L)
- 1531-5320
- OpenAlex
- W2742569683
- Cited by
- 1,627
- References
- 199
- FWCI
- 90.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, 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. https://doi.org/10.3758/s13423-017-1343-3
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