Sample overlap is a phenomenon where the same subject is sampled multiple times in a research study. It occurs when a study’s sample size is too small or when the same population is sampled multiple times to increase the sample size. Sample overlap can cause bias in the data, as the same individuals are more likely to have different responses to the same questions.

Sample overlap can be addressed in a variety of ways. The most common method is to randomly select a subset of the original sample for each iteration. This ensures that the same individuals are not repeatedly sampled. Another approach is to stratify the sample according to important characteristics such as gender, age, or ethnicity. This ensures that each group is represented in each iteration. Finally, researchers can use a larger sample size to reduce the likelihood of sample overlap.

Sample overlap can have a significant impact on research results. Studies that have significant sample overlap are less reliable than those with no overlap. Additionally, researchers should consider the potential for bias when designing studies with overlapping samples.

To conclude, sample overlap is an important phenomenon that can lead to biased results in research studies. Therefore, researchers should take steps to reduce the likelihood of sample overlap, such as using a larger sample size or stratifying the sample.


American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: Author.

Horton, N. J., & Lipsitz, S. R. (1996). Bias in sampling. American Journal of Public Health, 86(7), 945-951.

Luo, X., & Schaeffer, N. C. (2000). Sample overlap in survey research. Public Opinion Quarterly, 64(2), 130-148.

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