Small sample sizes refer to the limited number of observations or data points collected in a study, which can impact the reliability and validity of statistical inferences. In research, having a small sample size can lead to increased variability and less precise estimates, making it challenging to detect true effects or relationships. This concept is particularly relevant in non-parametric tests, which are often used when assumptions about population distributions cannot be met or when data is limited.
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