Outlier Exclusion Procedures Must Be Blind to the Researcher's Hypothesis

When researchers choose to identify and exclude outliers from their data, should they do so across all the data, or within experimental conditions? A survey of recent papers published in the Journal of Experimental Psychology: General shows that both methods are widely used, and common data visualization techniques suggest that outliers should be excluded at the condition-level. However, I highlight in the present paper that removing outliers by condition runs against the logic of hypothesis testing, and that this practice leads to unacceptable increases in false-positive rates. I demonstrate that this conclusion holds true across a variety of statistical tests, exclusion criterion and cutoffs, sample sizes, and data types, and shows in simulated experiments and in a reanalysis of existing data that by-condition exclusions can result in false-positive rates as high as 43%. I finally demonstrate that by-condition exclusions are a specific case of a more general issue: Any outlier exclusion procedure that is not blind to the hypothesis that researchers want to test may result in inflated Type I errors. I conclude by offering best practices and recommendations for excluding outliers.

Automated summary

Researchers should be cautious when choosing to exclude outliers, as condition-level exclusions may lead to inflated false-positive rates. The study highlights the importance of blind outlier exclusion procedures to avoid Type I errors.