Small sample sizes refer to a limited number of observations or data points collected in a study or experiment, often leading to challenges in statistical analysis and inference. In many cases, small sample sizes can result in less reliable estimates, increased variability, and reduced power to detect true effects or differences. This situation becomes particularly relevant when considering nonparametric tests, which are designed to be less sensitive to the distributional assumptions typically required for larger samples.
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