Small sample theory

Small sample theory involves statistical methods used when analyzing data drawn from a limited number of observations. Unlike large-sample methods that assume data are plentiful and stable, small sample techniques account for increased variability and uncertainty inherent in fewer data points. They often rely on specialized distributions, like the t-distribution, to produce more accurate estimates and confidence intervals, recognizing that small samples can produce less precise results. The core goal is to make valid inferences about a population even when only limited data are available, by adjusting methods to reflect the greater uncertainty inherent in small samples.