Many books on statistical methods advocate a 'conditional decision rule' when comparing two independent group means. This rule states that the decision as to whether to use a 'pooled variance' test that assumes equality of variance or a 'separate variance' Welch t test that does not should be based on the outcome of a variance equality test. In this paper, we empirically examine the Type I error rate of the conditional decision rule using four variance equality tests and compare this error rate to the unconditional use of either of the t tests (i.e. irrespective of the outcome of a variance homogeneity test) as well as several resampling-based alternatives when sampling from 49 distributions varying in skewness and kurtosis. Several unconditional tests including the separate variance test performed as well as or better than the conditional decision rule across situations. These results extend and generalize the findings of previous researchers who have argued that the conditional decision rule should be abandoned.

中文翻译：

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进一步评估条件决策规则，以比较两个独立的方法。

比较两种独立的群体均值时，许多有关统计方法的书都提倡“条件决策规则”。该规则指出，是否使用假定方差相等的“合并方差”检验或不使用方差均等检验结果的“单独方差” Welcht检验作为决策依据。在本文中，我们使用四个方差相等检验对条件决策规则的I类错误率进行实证检验，并将该错误率与t个检验中的任意一个无条件使用（即，不考虑方差均匀性检验的结果）进行比较，以及从偏斜度和峰度变化的49个分布中采样时的几种基于重采样的替代方法。多个无条件测试（包括单独的方差测试）在各种情况下的性能均优于或优于条件决策规则。这些结果扩展并概括了先前研究人员的研究结果，他们认为应该放弃条件决策规则。