Which approach helps avoid double-counting records when filtering LDS data?

Which approach helps avoid double-counting records when filtering LDS data?

• A. Use unique identifiers and apply filters without overlap; reset indices if needed.
• B. Apply filters in arbitrary order and sum overlapping results.
• C. Merge results from different filters and count duplicates.
• D. Remove duplicates after filtering without tracking identifiers.

Answer: (A)

Double-counting happens when a single record satisfies more than one filter and ends up in multiple result sets. The most reliable way to prevent this is to track each record with a unique identifier and design the filters so a record can belong to only one result set. That way, counting the records is straightforward and each one is counted once. Resetting the indices after filtering helps keep the data tidy, so counts aren’t skewed by how rows are numbered after filtering. In practice, you filter the data, use the unique IDs to count or merge results, and ensure you’re counting distinct records. This approach minimizes overlap and gives an accurate total.

Double-counting happens when a single record satisfies more than one filter and ends up in multiple result sets. The most reliable way to prevent this is to track each record with a unique identifier and design the filters so a record can belong to only one result set. That way, counting the records is straightforward and each one is counted once. Resetting the indices after filtering helps keep the data tidy, so counts aren’t skewed by how rows are numbered after filtering. In practice, you filter the data, use the unique IDs to count or merge results, and ensure you’re counting distinct records. This approach minimizes overlap and gives an accurate total.