June 5, 2026
Mark, restore, or delete low-quality responses
Bad responses skew results, but weeding them out shouldn't mean losing the data entirely. Researchers can now review, invalidate, restore, and permanently delete responses directly within a study:
Mark responses Invalid individually or in bulk, with a predefined reason: Duplicate, Test, Fraudulent, Failed Quality Check, Incomplete, or Other
Auto-exclusion from analytics, AI insights, reports, exports, and weighting, while the response stays available in the study
Restore at any time, and Workspace Admins can permanently delete when needed
The Study Overview now shows separate counts for Valid and Invalid responses, so only high-quality data feeds into analytics and reporting.