Small-sample stats are statistical methods designed for datasets with few observations, where standard tests may fail. Researchers use techniques like exact tests, bootstrapping, and Bayesian approaches to draw reliable inferences. These methods benefit scientists, clinicians, and analysts working with limited data in fields such as medicine, psychology, and ecology.
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