A Study on Power of Chi-Square and G Statistics in Biology Sciences

Abstract

The objective of this study was to examined Chi-Square and G test statistics in place of enough sample size, contingency coefficient and power of test for different four contingency tables (data set) regarding biology sciences. Besides, this study was to determine whether sample sizes of various four samples in biology sciences were sufficient. The reliability of two statistics related to Sample size, contingency coefficient and power of test. Power analysis for Chi-Square and G test statistics were performed using a special SAS macro According to results of power analysis, sample sizes of other sets of data except the third data set were determined to be sufficient because power values for both statistics were more than 88%. With respect to power analysis, G statistics for the initial two data sets were more advantageous than other as power value of G statistics were larger than that of other. In the last data set, as sample size were 1607 and power values for both statistics were 100%, both were asymptotically equivalent each other. As power values of the third data set for Chi-Square and G test statistics were approximately 46.77 and 58.16%, respectively, sample size with 20 for both were determined to be insufficient. When we artificially increased 30 to 200 by 10, sufficient sample size for third data should be 50 so as to provide power values of 80% with respect to results of SAS special macro. As a result, this study emphasized that researchers should have taken into sample sizes and power of test account except for probability of Type Error I in contingency tables in order to determine the best one of both statistics. © 2006 Asian Network for Scientific Information.