Quantitative Research Methods
Master surveys, analytics, and A/B tests to measure WHAT users do and HOW MANY at scale
Your Progress
Section 3 of 5Quantitative = Measuring WHAT and HOW MANY
Qualitative research tells you WHY. Quantitative research tells you WHAT and HOW MANY. It measures behavior at scale, validates hypotheses, and gives you the numbers to make data-driven decisions. But remember: numbers without context are meaningless. Always pair quant with qual.
Common Quantitative Methods
Each method measures different aspects of user behavior:
Surveys
Structured questions sent to many users to quantify opinions and behaviors
✅ Pros
- • Reach many users fast
- • Quantifiable data
- • Easy to analyze
- • Cheap per response
⚠️ Cons
- • Low response rates (5-15%)
- • No follow-up questions
- • Survey fatigue
- • Can't explore new topics
Common Metrics:
The A/B Testing Trap
❌ Common Mistake
Stopping test too early when you see a winner. Classic error that leads to false positives.
✅ Right Approach
Calculate required sample size BEFORE running test. Run until you hit that number, then decide.
A/B Test Duration Calculator
Calculate how long to run your A/B test based on traffic and expected improvement:
A/B Test Duration Calculator
✅ Likely Detectable
Numbers Don't Tell the Full Story
Analytics show conversion rate dropped 20%. That's WHAT happened. But WHY did it drop? That requires qualitative research. The best product decisions combine both: Use analytics to find the problem. Use interviews to understand the problem. Then build the solution.
Key Takeaways
- •Quantitative methods (surveys, analytics, A/B tests) measure WHAT users do and HOW MANY at scale.
- •Surveys validate hypotheses. Analytics track behavior. A/B tests prove which version performs better.
- •Calculate required sample size BEFORE running A/B tests. Don't stop early when you see a winner.
- •Numbers without context are meaningless. Always pair quantitative data with qualitative insights.