Society of Actuaries (SOA) PA Practice Exam

When examining bivariate relationships, why is it important to check for extreme outliers?

1. They can indicate potential data entry errors

2. They always enhance analysis quality

3. They are automatically excluded by software

4. They typically improve model accuracy

The correct answer is: They can indicate potential data entry errors

In the context of examining bivariate relationships, checking for extreme outliers is crucial because they can indicate potential data entry errors. Outliers may represent values that are significantly different from the rest of the data set, which can skew results and lead to misleading conclusions. Identifying these outliers allows analysts to investigate whether they are indeed valid observations or the result of errors in data collection or entry processes. While outliers can sometimes be valid and carry important information, they should be scrutinized carefully. They can disproportionately influence statistical measures, such as the mean and correlation coefficients, which could ultimately misrepresent the underlying relationship being studied. Therefore, recognizing that outliers can signal inaccuracies in the data encourages more careful validation of the dataset prior to proceeding with further analysis. This careful scrutiny helps ensure that the conclusions drawn from the analysis are based on accurate and representative data.