This test will detect outliers that are either much smaller or much larger than the rest of the data. Rosner’s approach is designed to avoid the problem of masking, where an outlier that is close in value to another outlier can go undetected. Rosner’s test is appropriate only when the data, excluding the suspected outliers, are approximately normally distributed, and when the sample size is greater than or equal to 25. Data should not be excluded from analysis solely on the basis of the results of this or any other statistical test. If any values are flagged as possible outliers, further investigation is recommended to determine whether there is a plausible explanation that justifies removing or replacing them. … Rosner’s Outlier Test google

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