minsamples

minsamples refers to a statistical approach or tool used to determine the minimum number of samples required to achieve a desired level of confidence or statistical power in a study. The concept is grounded in power analysis, which assesses the probability that a test will detect an effect of a given size if that effect truly exists. By calculating the smallest sample size that satisfies predefined significance (α) and power (1‑β) thresholds, researchers can design more efficient experiments, reduce unnecessary data collection, and conserve resources.

The methodology often employs formulas derived from the central limit theorem, t‑tests, ANOVA, regression, or non‑parametric tests, depending on the study design. Inputs required include an expected effect size, variability of the population, acceptable error rates, and desired power. Effect size can be expressed as Cohen’s d, eta‑squared, odds ratios, or other metrics appropriate to the analysis.

Several software packages incorporate minsamples functionality, including the GNU Scientific Library, R’s pwr and G*Power, and Python libraries such as statsmodels and scikit‑learn. These tools allow users to conduct exploratory “what‑if” analyses, comparing different scenarios of sample size, effect size, and significance levels.

In practice, minsamples is used across fields such as psychology, medicine, engineering, and market research. It helps avoid both under‑powered studies, which risk false negatives, and over‑powered studies, which waste time and money. By providing a clear, quantitative benchmark for sample size, minsamples contributes to the rigor and reproducibility of empirical research.