Why Representativeness Should Be Avoided
Open accessAbstract
The essence of knowledge is generalisation. That rubbing wood in a certain way can produce fire is a knowledge derived by generalisation from individual experiences; the statement means that rubbing wood in this way will always produce fire. The art of discovery is therefore the art of correct generalisation. What is irrelevant, such as the particular shape or size of the piece of wood used, is to be excluded from the generalisation; what is relevant, for example, the dryness of the wood, is to be included in it. The meaning of the term relevant can thus be defined: that is relevant which must be mentioned for the generalisation to be valid. The separation of relevant from irrelevant factors is the beginning of knowledge. —Hans Reichenbach1 Why do so many believe that selecting representative study populations is a fundamental research aim for scientific studies? This view is widely held: representativeness is exalted along with motherhood, apple pie and statistical significance. For some researchers this goal can be so important that they would deem a study not worth undertaking if representativeness cannot be achieved. That was the case for two advisors to the U.S. National Children’s Health Study, who resigned when the study design was changed so that representativeness was threatened.2 We admire people who take a stand for principle over expediency, but what exactly is the principle that representativeness embodies? Here we suggest that representativeness may be essential for conducting opinion polls, or for public-health applications, but it is not a reasonable aim for a scientific study.
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Homogenous samples can be preferable to better control for confounders in research aiming to identify causal mechanisms.
“while homogenous samples can be preferable to better control for confounders in research aiming to identify causal mechanisms [6]”
From Hasty generalizations and generics in medical research: A systematic review (2024) · cites this paper as [6]
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- Authors
- Rothman, K. J., Gallacher, J. E., & Hatch, E. E.
- Affiliations
- Boston University (US)
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-08-01
- Volume
- 42
- Issue
- 4
- Pages
- 1012-1014
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC3888189
- OpenAlex
- W2142509258
- Cited by
- 861
- References
- 6
- Citations by year
- 2026: 38 · 2025: 59 · 2024: 76 · 2023: 96 · 2022: 110 · 2021: 83 · 2020: 74 · 2019: 71 · 2018: 71 · 2017: 58 · 2016: 43 · 2015: 41 · 2014: 31 · 2013: 9
- FWCI
- 36.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Epidemiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (5r01hd060680-05)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01-HD060680)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- representativeness, generalization, research design, public opinion survey, public health applications
- MeSH terms
- Sampling Studies, Statistics as Topic, Epidemiologic Studies
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