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Activity Number: 552
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #308774
Title: Parametric Tests for Two Population Means: An Empirical Comparison of Type I Error Control and Statistical Power
Author(s): Patricia Rodriguez de Gil*+ and Yi-Hsin Chen and Eun Sook Kim and Diep Nguyen and Anh Kellermann and Aarti Bellara and Jeffrey D. Kromrey
Companies: University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida
Keywords: Type I Error Control ; Statistical Power ; Parametric Tests ; Satterthwaite's Approximate Test ; Conditional T-test
Abstract:

A simulation study was conducted to explore the Type I error rates and statistical power of the independent means t-test, Satterthwaite's approximate t-test, and the conditional t-test based on a preliminary test of variances. Factors manipulated were total sample size (10, 20, 50, 100, 200, 300, 400), sample size ratio between the groups (1:1, 2:3, 1:4), population variance ratio (1:1, 1:2, 1:4, 1:8, 1:12, 1:16, 1:20), population effect size (0, .2, .5, .8), and alpha for both the test of treatment effect and the test of variances. Normal population distributions and distributions with varying kurtosis and skewness values (?1=1.00, ?2=3.00; ?=1:50, ?2=5:00; ?=2.00, ?2=6.00; ?1=0.00, ?2=25.00) were included. As expected, the independent means t-test showed great dispersion of Type I error control. The Satterthwaite t-test provided adequate Type I error control in nearly all conditions and the conditional t-test evidenced notable improvement in Type I error control relative to the independent means t-test as the level of alpha for the test of variances increased. This study provides guidance on the proper use of parametric tests with nonnormal, heteroscedastic populations.


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