探討兩組或多組母體平均數是否有差異性，傳統方法是用點假設檢定。然而在點假設檢定中，只要樣本數夠大，就會拒絕虛無假設，因此許多研究轉而利用區間假設檢定來取代。本文利用概度比 (LR) 檢定的方法，推導出針對一群母體平均數分佈在非不同區域的常態分佈母體，其等價性的檢定程序。並以蒙地卡羅模擬方法對LR檢定和Bau, Chen和Xiong (1993) 所推導出的student化全距檢定法做比較。由電腦模擬的結果顯示，student化全距檢定法只有在LFC的均數結構下，才有檢定的名目水準。LR檢定程序雖然略顯不太保守，但當樣本數夠大時，在虛無假設成立的條件下皆能得到檢定的名目水準，並比student化全距檢定程序更具檢定力。而且LR檢定程序易於施行，直接使用現有機率分配表格，不需要另外造表，亦無繁雜的計算。另外，亦可應用在母體數k≥2的情形。 The classical hypothesis for testing the difference between two or several normal means is to test the null hypothesis that the population means are equal. However, the null hypothesis will always be rejected for a large enough sample size. We derive likelihood ratio (LR) tests for the null hypothesis of equivalence that the normal means fall into a practical indifference zone. Also, we carry out an extensive simulation study to compare the performance of the LR test and the studentized range test of Bau, Chen and Xiong(1993). Simulation results indicate that the nominal level of the studentized range test occurs only under the least favorable configuration of means. The LR test might be slightly anticonservative statistically, but when the sample sizes are large, it always produces the nominal level for mean configurations under the null hypothesis, more powerful than the studentized range test. The LR test can easily be constructed and is a straightforward application that requires only current existing statistical tables, with no complicated computations. Moreover, the LR test can applied to k≥2 treatments.