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This research investigates the distributional characteristics of achievement and psychometric measures in large samples.

The unicorn, the normal curve, and other improbable creatures.

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Investigation reveals significant nonnormality in achievement and psychometric measures, suggesting reevaluation of statistical robustness.

This research investigates the distributional characteristics of achievement and psychometric measures in large samples.
Analyzed 440 large-sample achievement measures and psychometric measures for normality.
Identified various classes of contamination affecting distributions.
Statistical significance assessed at the alpha .01 level.
All measures found to be significantly nonnormal at alpha .01.
Detected tail weights from uniform to double exponential and severe digit preferences.
Findings challenge existing robustness research assumptions on normality in statistical analyses.

Theodore Micceri (1989) studied this question.

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Investigation reveals significant nonnormality in achievement and psychometric measures, suggesting reevaluation of statistical robustness.