CoChat
Sign Up
Cited by 1,734Statistical Methods and Bayesian InferenceBayesian Modeling and Causal InferenceProbability and Statistical Research

Bayesian statistical inference for psychological research.

Edwards, Ward, Lindman, Harold, Savage, Leonard J.·1963·Psychological Review
DOI 10.1037/h0044139
Open this paper in CoChat — free account

Abstract

Bayesian statistics, a currently controversial viewpoint concerning statistical inference, is based on a definition of probability as a particular measure of the opinions of ideally consistent people. Statistical inference is modification of these opinions in the light of evidence, and Bayes’ theorem specifies how such modifications should be made. The tools of Bayesian statistics include the theory of specific distributions and the principle of stable estimation, which specifies when actual prior opinions may be satisfactorily approximated by a uniform distribution. A common feature of many classical significance tests is that a sharp null hypothesis is compared with a diffuse alternative hypothesis. Often evidence which, for a Bayesian statistician, strikingly supports the null hypothesis leads to rejection of that hypothesis by standard classical procedures. The likelihood principle emphasized in Bayesian statistics implies, among other things, that the rules governing when data collection stops are irrelevant to data interpretation. It is entirely appropriate to collect data until a point has been proven or disproven, or until the data collector runs out of time, money, or patience.

Integrity signals

Registry

Crossref · 10.1037/h0044139

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • P-values arguably overstate the evidence for an effect even when there is no model uncertainty.

    “In fact, p -values arguably overstate the evidence for an effect even when there is no model uncertainty (Edwards, Lindman and Savage, 1963; Berger and Delampady, 1987; Berger and Sellke, 1987).”

    From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Edwards, Lindman and Savage, 1963

  • Researchers may demonstrate the robustness of their results by using Bayesian hypothesis tests, because optional stopping is not problematic for Bayesian hypothesis testing.

    “They may also demonstrate the robustness of their results by using Bayesian hypothesis tests, because optional stopping is not problematic for Bayesian hypothesis testing”

    From Does preregistration improve the credibility of research findings? (2020) · cites this paper as Edwards, Lindman, & Savage, 1963, p. 193

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Edwards, W., Lindman, H., & Savage, L. J.
Affiliations
University of Michigan (US)
Venue
Psychological Review
Publisher
American Psychological Association (APA)
Published
1963-05
Volume
70
Issue
3
Pages
193-242
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
DOI
10.1037/h0044139
OpenAlex
W1967485420
Cited by
1,734
References
1
Citations by year
2026: 19 · 2025: 19 · 2024: 30 · 2023: 37 · 2022: 52 · 2021: 54 · 2020: 62 · 2019: 90 · 2018: 102 · 2017: 69 · 2016: 65 · 2015: 47 · 2014: 58 · 2013: 47 · 2012: 70
FWCI
34.85
Type
journal article
Language
en
Open access
closed
Locations
Psychological Review (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Keywords
Bayesian analysis, statistical inference, Bayes' theorem, prior distributions, uniform prior, likelihood principle, null hypothesis testing, stopping rules, psychology research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review