Financial managers routinely use the one-sample t-test to evaluate whether the mean returns of investment assets, strategies or funds are significantly different from zero. Simultaneously, however, they often ignore the fact that its application is not generally justified, in other words, that its usefulness depends on the properties of the population. We show by Monte Carlo simulation that, especially in skewed and/or autocorrelated populations, test decisions based on the t-test can be severely biased. More specifically, for sample sizes typically used in investment performance evaluation, the probability of falsely diagnosing a significant mean return–the false discovery rate–is significantly higher than the nominal error probability set in testing. We additionally illustrate that the popular empirical practices of (i) replacing the t-quantile with the standard normal quantile and (ii) removing outliers before conducting the t-test have crucial elevating impact on the false discovery rate.

财务经理通常使用一样本t检验来评估投资资产，策略或基金的平均收益是否显着不同于零。但是，与此同时，他们经常忽略这样一个事实，即其应用通常不合理，换句话说，其有用性取决于人口的性质。我们通过蒙特卡洛模拟显示，尤其是在偏斜和/或自相关的总体中，基于t检验的测试决策可能会严重偏差。更具体地说，对于通常用于投资绩效评估的样本量，错误诊断重大平均回报的可能性（错误发现率）明显高于测试中设置的名义错误概率。