Why is it important to check for outliers when performing a correlation or regression analysis?

When performing correlation or regression analysis, it is crucial to check for outliers because they can dramatically affect the results, potentially leading to misleading conclusions. Outliers are data points that significantly differ from the rest of the dataset. Their presence can skew the results of statistical calculations, such as correlation coefficients or regression lines, leading to a distorted understanding of the relationship between variables.

For example, if an outlier has an extreme value, it might inflate or deflate the correlation coefficient, making it appear stronger or weaker than it truly is. This could result in false interpretations about the strength and direction of the relationship, which is problematic when making decisions based on the analysis. Checking for outliers allows analysts to understand the complete data structure, decide whether to include or exclude certain points, and apply corrective measures if necessary, ensuring that the analysis is robust and reliable.

Consequently, failing to account for outliers may produce results that do not accurately reflect the underlying data, which is why recognizing their influence is essential in any analytical process.