Final answer:
A valid reason to remove an outlier is if it's a result of incorrect data entry. Outliers can impact the statistical analysis significantly, but they should be examined to determine if they represent true variation or an error before removal.
Step-by-step explanation:
A good reason to remove an outlier from your data set is if the outlier is an incorrectly-entered data, not real data. An outlier can significantly affect the results of statistical analysis, such as the slope of the least-squares regression line and the correlation coefficient, r. In situations where it is clear that the outlier is a result of a mistake or improper data entry, it is justifiable to remove it from the data set.
However, if an outlier occurs due to variation in the actual data, it is often important to investigate further before deciding to remove it. Outliers may represent a meaningful aspect of the population under study, possibly indicating an exceptional case or an alternative trend that could be important for understanding the overall data.
Different tests, such as examining if a data value is more than two standard deviations away from the predicted value on the regression line or assessing the impact on the interquartile range (IQR), can be used to determine if a data point is an outlier. These methods should be used to critically assess whether the outlier is a result of a genuine variation in data or an error before it is removed.