Outliers and their effect on analyses

• A. Observations far from others; can distort mean and p-values; may require Winsorizing or robust methods or investigation.
• B. Observations far from others always indicate a data entry error.
• C. Outliers have no effect on p-values when sample size is large.
• D. Outliers should always be discarded.

Answer: (A)

Outliers can heavily influence analyses because they pull estimates away from the bulk of the data. When a point lies far from the rest, it can tug the mean toward it, change the variance, and thus alter test statistics and p-values. Because of this, it’s important to investigate whether an outlier is a data entry error, a rare but real value, or something that should be handled differently. Depending on the situation, you might Winsorize extreme values, use robust methods that are less sensitive to outliers (such as median-based summaries or robust regression), or apply transformations. Outliers aren’t necessarily mistakes, and large sample size doesn’t guarantee p-values won’t be affected; simply discarding outliers is not always appropriate.