How do outliers influence data analysis?

Outliers can significantly influence data analysis by skewing results and leading to inaccurate conclusions. These extreme values can distort statistical measures such as the mean and standard deviation, which are sensitive to them. For instance, if a dataset contains a few exceedingly high or low values, the mean might not be representative of the majority of the data, conveying a misleading picture. This distortion can affect regression analyses, hypothesis tests, and other statistical tools, resulting in erroneous insights or decisions based on flawed data interpretation.

Overall, understanding and appropriately handling outliers is crucial in ensuring the validity and reliability of analytical outcomes. By acknowledging their potential to skew results, analysts can take steps to mitigate their impact, such as using robust statistical techniques or transforming the data to accommodate for these extreme values.