In this paper, 91 different tests for exponentiality are reviewed. Some of the tests are universally consistent while others are against some special classes of life distributions. Power performances of 40 of these different tests for exponentiality of datasets are compared through extensive Monte Carlo simulations. The comparisons are conducted for different sample sizes of 10, 25, 50 and 100 for different groups of distributions according to the shape of their hazard functions at 5 percent level of significance. Also, the techniques are applied to two real-world datasets and a measure of power is employed for the comparison of the tests. The results show that some tests which are very good under one group of alternative distributions are not so under another group. Also, some tests maintained relatively high power over all the groups of alternative distributions studied while some others maintained poor power performances over all the groups of alternative distributions. Again, the result obtained from real-world datasets agree completely with those of the simulation studies.

本文回顾了 91 种不同的指数检验。一些测试是普遍一致的，而另一些则针对某些特殊类别的生命分布。通过广泛的 Monte Carlo 模拟，比较了其中 40 种不同数据集指数测试的功率性能。对 10、25、50 和 100 的不同样本量进行比较，根据 5% 显着性水平的风险函数形状，对不同的分布组进行比较。此外，这些技术应用于两个真实世界的数据集，并采用功率测量来比较测试。结果表明，在一组替代分布下非常好的一些测试在另一组下不是很好。还，一些测试在所研究的所有替代分布组上保持相对较高的功率，而另一些测试在所有替代分布组上保持较差的功率性能。同样，从现实世界数据集中获得的结果与模拟研究的结果完全一致。