How are null values processed when grouping records?

When grouping records in data analysis, null values are typically removed from the analysis. This is because null values represent missing or undefined data, and including them in a grouping operation would not provide meaningful insights or metrics. In most data analysis tools and programming languages, null values are considered separate from any other values and do not contribute to aggregations, counts, or any calculations associated with grouped data.

This handling ensures that the results of the grouping reflect only the records that contain valid data. For example, when counting the number of occurrences of different categories in a dataset, null values would simply not be counted, allowing for a clear and accurate representation of the data at hand.

In contrast, the other options suggest various treatments of null values that are not standard practice in data grouping. For instance, including them by default or flagging them for review may occur in other contexts but does not apply to the actual grouping process itself in typical analytical workflows.