In a DATA step, how are missing values typically handled?

In a DATA step, handling missing values is a crucial aspect of data manipulation and analysis. The correct approach involves using conditional logic, such as the IF statement, to check for and replace missing values as needed. This allows for customizing the handling of missing values based on specific criteria or business rules.

For instance, one might check if a variable is missing and then assign it a default value or perform additional calculations. This flexibility is important because simply ignoring or assuming a default condition for missing values may lead to inaccurate outcomes.

In contrast, while the MISSING function could indeed be useful in identifying missing values, it does not directly address the action taken in dealing with them. There's also the assumption that the program should define these missing values as zero, which may not be appropriate in all scenarios. Lastly, while some operations may naturally exclude missing values during processing, they are not inherently ignored without explicit instructions in the DATA step. Thus, relying on the IF statement provides a proactive and precise means of managing missing data.