I fumbled the opener a bit, started talking about sample sizes before actually addressing the tension.
Acknowledge that qualitative and quantitative data often measure different things and can both be valid. Propose a structured approach to investigate the discrepancy, triangulate findings, and make a decision that balances both perspectives. Emphasize the importance of understanding the 'why' behind the numbers and the context of the qualitative feedback.
Pro tip: Frame the contradiction as an opportunity to uncover deeper insights rather than a problem to be resolved. Show that you can synthesize both types of data to make more robust product decisions.
Recognize that qualitative and quantitative data provide different lenses and both are valuable. Avoid dismissing either source outright; instead, treat the contradiction as a signal to dig deeper.
Examine the data collection methods, sample sizes, and potential biases in both qualitative and quantitative sources. Look for segmentation or contextual factors that might explain the difference.
Seek additional data points (e.g., user interviews, surveys, behavioral analytics) to confirm or refute each source. Use mixed-methods research to build a more complete picture.
Weigh the evidence and consider the product goals and user impact. Make a decision that leverages the strengths of both data types, and be transparent about the trade-offs.
Implement the decision with measurable outcomes and continue to collect both qualitative and quantitative feedback. Be prepared to pivot if new evidence emerges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.