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Cited by 737

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Comparing the Means of Several Groups

Godfrey, Katherine·1985·New England Journal of Medicine

Abstract

This article discusses statistical methods for comparing the means of several groups and focuses on examples from 50 Original Articles published in the Journal in 1978 and 1979. Although medical authors often present comparisons of the means of several groups, the most common method of analysis, multiple t-tests, is usually a poor choice. Which method of analysis is appropriate depends on what questions the investigators wish to ask. If the investigators want to identify which of the groups under study are different from the rest, they will need a different method from the one required if they wish simply to decide whether or not the groups share a common mean. More complicated questions about the group means call for more sophisticated techniques. Of the 50 Journal articles examined, 27 (54 per cent) used inappropriate statistical methods to analyze the differences between group means. Investigators need to become better acquainted with statistical techniques for making multiple comparisons between group means.

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Metadata

Authors
Godfrey, K.
Venue
New England Journal of Medicine
Published
1985-12-05
Volume
313
Issue
23
Pages
1450-1456
ISSN
  • 0028-4793 (ISSN-L)
  • 1533-4406
OpenAlex
W1976018371
Cited by
737
References
1
FWCI
76.84
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Comparing the Means of Several Groups. https://app.cochat.ai/papers/10.1056/nejm198512053132305
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Godfrey, K. (1985). Comparing the Means of Several Groups. New England Journal of Medicine. https://doi.org/10.1056/nejm198512053132305

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.