If a researcher finds multiple outliers in their data set, what should they consider?

When a researcher encounters multiple outliers in their data set, it is crucial to reassess the assumptions underlying their statistical tests. Outliers can indicate that certain assumptions may not hold true, such as the assumptions of normality, homoscedasticity, or linearity, depending on the statistical methods being employed.

For example, many parametric tests assume that data follows a normal distribution; the presence of outliers can violate this assumption, leading to inaccurate results. By reconsidering these assumptions, the researcher can determine whether the statistical methods applied are appropriate for the data at hand or whether alternative approaches or transformations may be necessary to appropriately analyze the data while accounting for the outliers.

While additional data collection or choosing different statistical analyses may be valid considerations, they do not directly address the fundamental issues that outliers bring to light regarding the assumptions of current methodologies. Simply using the mean to analyze the data also overlooks the potential influence of outliers on the overall data representation, and ignoring them could lead to misleading conclusions. Therefore, rethinking the assumptions is the most comprehensive and statistically sound approach when faced with multiple outliers.