Society of Actuaries (SOA) PA Practice Exam 2025 – Comprehensive All-in-One Guide to Mastering Your Exam Success!

Removing a column from a dataset is often considered based on the proportion of missing data it contains. When a column has more than 50% missing data, it generally suggests that the information the column could provide is insufficient for analysis or modeling. Retaining such a column can lead to complications in analysis, as it may skew results or require extensive techniques to handle the missing values.

In contrast, having less than 5% missing data is typically manageable, and imputation techniques can be applied, making it preferable to keep such a column for the insights it may provide. A column that contains only a single factor level offers no variability or information and should also be removed, but this is not directly related to missing data analysis. Similarly, whether values are all continuous does not justify removal if the column otherwise contributes valuable information to the dataset. Thus, the scenario where more than 50% of data is missing offers strong justification for removing the column due to its limited usefulness.