Hypothesis testClassical statistics

Robust One-Sample t-test (Trimmed Mean)

The robust one-sample t-test replaces the ordinary mean with a trimmed mean and the sample variance with a Winsorized variance to compare a population location against a hypothesized value. It retains the t-test decision framework while sharply reducing sensitivity to outliers and heavy-tailed distributions, making it reliable in real-world continuous data that deviate from normality.

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Sources

  1. Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
  2. Yuen, K. K. (1974). The two-sample trimmed t for unequal population variances. Biometrika, 61(1), 165–170. DOI: 10.1093/biomet/61.1.165

Related methods

Referenced by

ScholarGateRobust one-sample t-test (Robust One-Sample Location Test Using Trimmed Mean). Retrieved 2026-06-04 from https://scholargate.app/en/statistics/robust-one-sample-t-test