In analytical methods, the presence of outliers may mislead the experimenters. The existing Cochran’s tests apply to detect outliers when cent percent of observations in the data are determinate. But, when some or all observations are not precise, determined, and certain, the existing Cochran’s tests cannot be applied for detecting outliers in the data. In this paper, we introduce Cochran’s tests and modification of Cochran’s tests based on Levene’s test under the neutrosophic statistics. We present the design, implication, and decision criterion of both tests under an indeterminacy situation. The performances of both tests are evaluated using the power curves for various non-normal distributions. The comparison of both Cochran’s tests is given and explained with the aid of a real example.

在分析方法中，异常值的存在可能会误导实验者。当确定数据中百分之一的观测值时，现有的Cochran检验适用于检测异常值。但是，当某些或所有观测值不精确，无法确定并确定时，现有的Cochran检验将无法应用于检测数据中的异常值。在本文中，我们介绍了Cochran检验和基于中智统计的Levene检验对Cochran检验的修改。我们介绍了不确定情况下这两种测试的设计，含义和决策标准。使用各种非正态分布的功率曲线来评估这两个测试的性能。借助于一个真实的例子，给出并解释了两个Cochran测试的比较。