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. 2024 Sep 6;29(9):761-763.
doi: 10.1093/oncolo/oyae162.

Why is a small sample size not enough?

Ying Cao  1 , Ronald C Chen  1 , Aaron J Katz  1   2
Affiliations

Why is a small sample size not enough?

Ying Cao et al. Oncologist. .

Abstract

Background: Clinical studies are often limited by resources available, which results in constraints on sample size. We use simulated data to illustrate study implications when the sample size is too small.

Methods and results: Using 2 theoretical populations each with N = 1000, we randomly sample 10 from each population and conduct a statistical comparison, to help make a conclusion about whether the 2 populations are different. This exercise is repeated for a total of 4 studies: 2 concluded that the 2 populations are statistically significantly different, while 2 showed no statistically significant difference.

Conclusions: Our simulated examples demonstrate that sample sizes play important roles in clinical research. The results and conclusions, in terms of estimates of means, medians, Pearson correlations, chi-square test, and P values, are unreliable with small samples.

Keywords: data analysis; random sample; sample size; simulation; statistics.

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Conflict of interest statement

The authors indicated no financial relationships.

Figures

Figure 1.
Figure 1.
(A) Two random samples of n = 10 each were drawn from 2 normally distributed populations each with N = 1000. Population 1 has means 0 and SD 1, and population 2 has mean 0.5 and SD 0.5. (B-D) Images illustrate new random samples using the same methodology as in panel (A).
See this image and copyright information in PMC

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