Sample Size
The number of participants in a research study. Appropriate sample size depends on research goals, method type (qualitative vs. quantitative), the precision required, and the number of distinct user segments being studied.
Definition: The number of participants in a research study. Appropriate sample size depends on research goals, method type (qualitative vs. quantitative), the precision required, and the number of distinct user segments being studied.
Sample size—how many participants you need—is one of the most frequently asked questions in UX research. The answer depends on what you are trying to accomplish.
Qualitative Research
For qualitative research like interviews and moderated testing:
- 5 users per segment: Identifies approximately 85% of common usability problems (the "rule of five")
- 10-30 participants: Typical range for reaching saturation in interview studies
- More segments = more participants: Sample size multiplies with each distinct user group
The "rule of five" applies specifically to finding usability problems, not to measuring satisfaction, validating market need, or statistical generalization.
Quantitative Research
For quantitative research like surveys and benchmarking:
- 30+ participants: Minimum for basic statistical analysis
- 100-200+ participants: Required for reliable comparisons and narrow confidence intervals
- Larger samples for small effects: Detecting subtle differences requires more participants
Statistical power analysis can help determine exact requirements based on your desired precision.
The Real Question
The critical question is not "how many?" but "how many of whom?" If your product serves different user types, you need adequate representation of each segment.
For the statistical foundations behind sample size calculation, see Sample Size Formulas Explained.
Related Terms
Quantitative Research
Research focused on numerical measurement with the goal of generalizing findings from a sample to a broader population. Answers 'how much,' 'how many,' and 'how often.'
Qualitative Research
Research focused on understanding the 'what' and 'why' through rich stories, observations, and context. Seeks depth of understanding rather than statistical measurement.
Statistical Significance
A determination that an observed result is unlikely to have occurred by random chance alone. Conventionally indicated by a p-value below 0.05, meaning less than 5% probability of the result being a fluke.
Segmentation
Dividing your user base into distinct groups based on shared characteristics, behaviors, or needs. The foundation for targeted research, personalized experiences, and meaningful sample design.
Mentions in the Knowledge Hub
This term is referenced in the following articles:
Research Method Explorer
An interactive tool that guides you to the right UX research method based on your goals, constraints, and context.
Building a Research Career in the Age of AI
AI is transforming what researchers do daily, but it amplifies rather than replaces the core value researchers provide. Understanding which skills remain essential and how to grow them is critical for career development in this changing landscape.