Robust statistics are procedures that maintain nominal Type I error rates and statistical power in the presence of violations of the assumptions that underpin parametric inferential statistics. Since George Box coined the term in 1953, research on robust statistics has centered on the assumption of normality, although the violation of other parametric assumptions (e.g., homogeneity of variance) has their own implications for the accuracy of parametric procedures. This entry looks at the importance of robust statistics in educational and social science research and explains the robustness argument. It then describes robust descriptive statistics, their inferential extensions, and two common resampling procedures that are robust alternatives to classic parametric methods.
Robust statistics are important tools for educational and social science researchers because of three well-established findings. First, ...
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