How does a researcher mitigate the impact of outliers on their analysis?

Mitigating the impact of outliers on data analysis is crucial because outliers can significantly distort the results, leading to misleading conclusions. Conducting separate analyses without the outliers allows the researcher to see how the overall results change with and without these extreme values. This approach provides a clearer picture of the data's central tendency and variability, ensuring that conclusions drawn from the analysis are robust and not overly influenced by anomalous observations.

By comparing the results of the analysis with and without outliers, a researcher can determine whether those outliers are influential in a meaningful way or if they simply represent anomalies that do not reflect the underlying phenomena being studied. This method promotes a more reliable interpretation of data, helping to verify the validity of the findings.

The other options, while potentially relevant in certain contexts, do not address the issue of outlier impact as effectively as conducting separate analyses. This method strikes a balance between acknowledging the presence of outliers and ensuring that they do not mislead the overall interpretation of the data.