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.
For qualitative research like interviews and moderated testing:
The "rule of five" applies specifically to finding usability problems, not to measuring satisfaction, validating market need, or statistical generalization.
For quantitative research like surveys and benchmarking:
Statistical power analysis can help determine exact requirements based on your desired precision.
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.
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.'
Research focused on understanding the 'what' and 'why' through rich stories, observations, and context. Seeks depth of understanding rather than statistical measurement.
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.
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.
This term is referenced in the following articles:
Validity is not one thing. It is a family of concepts, each addressing a different way your research can go wrong. A comprehensive guide to study design validity, measurement validity, qualitative trustworthiness, and modern unified frameworks.
An interactive tool that guides you to the right research method based on your goals, constraints, and context.
AI changes what researchers do and how many are needed. Productivity gains are real, and teams are getting leaner. But the skills that remain essential, strategic thinking, stakeholder influence, methodological judgment, and ethical reasoning, are precisely the ones AI cannot replicate. No guarantees, but building those skills is the best bet you have.