How sample size influences research outcomes

By J. Faber, L. M. Fonseca. 2014

901 citations recorded by the source corpus.

Sample size calculation is part of the early stages of conducting an epidemiological, clinical or lab study. In preparing a scientific paper, there are ethical and methodological indications for its use. Two investigations conducted with the same methodology and achieving equivalent results, but different only in terms of sample size, may point the researcher in different directions when it comes to making clinical decisions. Therefore, ideally, samples should not be small and, contrary to what one might think, should not be excessive. The aim of this paper is to discuss in clinical language the main implications of the sample size when interpreting a study.

Subfields: Statistics and Probability

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “This has the practical effect of reducing cohort size, limiting statistical significance, preventing the detection of weak effects [106], and restricting the number of parameters that can be trained in a model.”
Evaluation of a decided sample size in machine learning applications (2023, background): “In fact, Faber and Fonseca [14] demonstrated that increasing sample size beyond a range might not significantly improve results.”