How many discoveries have been lost by ignoring modern statistical methods

Hundreds of articles in statistical journals have pointed out that standard analysis of variance, Pearson productmoment correlations, and least squares regression can be highly misleading and can have relatively low power even under very small departures from normality. In practical terms, psychology journals are littered with nonsignificant results that would have been significant if a more modern method had been used. Modern robust techniques, developed during the past 30 years, provide very effective methods for dealing with nonnormality, and they compete very well with conventional procedures when standard assumptions are met. In addition, modern methods provide accurate confidence intervals for a much broader range of situations, they provide more effective methods for detecting and studying outliers, and they can be used to get a deeper understanding of how variables are related. This article outlines and illustrates these results.

[1]  Frank E. Harrell,et al.  A new distribution-free quantile estimator , 1982 .

[2]  P. Rousseeuw,et al.  Unmasking Multivariate Outliers and Leverage Points , 1990 .

[3]  Rand R. Wilcox,et al.  Statistics for the Social Sciences , 1996 .

[4]  Frederick Mosteller,et al.  Exploring Data Tables, Trends and Shapes. , 1986 .

[5]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[6]  R. Wilcox A Note on the Theil-Sen Regression Estimator When the Regressor Is Random and the Error Term Is Heteroscedastic , 1998 .

[7]  Richard E. Snow,et al.  Pygmalion and Intelligence? , 1995 .

[8]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[9]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[10]  Werner A. Stahel,et al.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[11]  J. Booth,et al.  Resampling-Based Multiple Testing. , 1994 .

[12]  Joyce Snell,et al.  6. Alternative Methods of Regression , 1996 .

[13]  H. J. Whitford,et al.  How to Use the Two Sample t‐Test , 1986 .

[14]  Douglas M. Hawkins,et al.  Comparison of Model Misspecification Diagnostics Using Residuals from Least Mean of Squares and Least Median of Squares Fits , 1992 .

[15]  R. Wilcox Introduction to Robust Estimation and Hypothesis Testing , 1997 .

[16]  K. Yuen,et al.  The two-sample trimmed t for unequal population variances , 1974 .

[17]  Frederick Mosteller,et al.  Understanding robust and exploratory data analysis , 1983 .

[18]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[19]  R. Wilcox Tests of Independence and Zero Correlations Among P Random Variables , 1997 .

[20]  Simon J. Sheather,et al.  The Use and Interpretation of Residuals Based on Robust Estimation , 1993 .

[21]  B. Iglewicz,et al.  Bivariate extensions of the boxplot , 1992 .

[22]  S. Sheather,et al.  Robust Estimation and Testing , 1990 .