Effective Sample Sizes for the Test of Mean Differences Based on Homogeneity Test

다국어 초록 (Multilingual Abstract)

Many researchers in various study fields use the two sample 􀆒 􀃠test to confirm their treatment effects. The two sample 􀆒 􀃠 test is generally used for small samples, and assumes that two independent random samples are selected from normal populations, and the population variances are unknown. Researchers often conduct 􀂟 􀃠test, the test of equality of variances, before testing the treatment effects, and the test statistic or confidence interval for the two sample 􀆒 􀃠test has two formats according to whether the variances are equal or not. Researchers using the two sample 􀆒 􀃠test often want to know how large sample sizes they need to get reliable test results. This research gives some guidelines for sample sizes to them through simulation works. The simulation had run for normal populations with the different ratios of two variances for different sample sizes (􀀽 􀃐􀃗 ). The simulation results are as follows. First, if one has no idea equality of variances but he/she can assume the difference is moderate, it is safe to use sample size at least 20 in terms of the nominal level of significance. Second, the power of 􀂟 􀃠test for the equality of variances is very low when the sample sizes are small (􀃯 􀃐􀃗) even though the ratio of two variances is equal to 􀃏. Third, the sample sizes at least 􀃎􀃗 for the two sample 􀆒 􀃠 test are recommendable in terms of the nominal level of significance and the error limit.

참고문헌 (Reference)

1 Student, "The probable error of mean" 6 : 1-25, 1908

2 J. Pfanzagl, "Studies in the history of probability and statistics XLIV, A forerunner of the t-distribution" 83 : 891-898, 1996

3 G. Casella, "Statistical Inference" Brooks/Cole Publishing Company 1990

4 The Korean Statistical Society, "Statistical Glossary" Freeacademy Inc 1987

5 J. E. Freund, "Mathematical Statistics" Prentice-Hall International, Inc. 1992

6 S. H. Park, "Design of Experiments" Minyeongsa 2003

7 F. E. Satterthwaite, "An approximate distribution of estimates of variance components" 2 : 110-114, 1946