What you can learn from 27 random sets of participants
With Usabilla you combinate quantitative and qualitative research. The results from a larger group of participants are visualized with colorful heatmaps and measurable scatterplots. How many participants should you use for a typical test? It’s quite hard to give one single answer to this question. The number of participants really depends on the purpose of your test and the type of questions you’re using. To give you an indication about trends we’ve plotted 27 random samples of an open question in a large demo case with over 500 participants.
In these five random samples with 5 participants you don’t see any clear patterns. There are no obvious trends and the points don’t really cluster.
In these samples with 10 random participants some vague patterns are recognizable. There’s still no good overlap in the different samples, but you could identify some basic trends.
The samples with 20 participants show interesting trends. The results look quite the some. If you want to really quantify results there are still obvious differences between the individual samples.
There’s an interesting overlap in the random samples of 50 participants. With these samples sizes you could quantify results. You can still see some slight differences in specific (smaller) areas of the visualizations.
These random samples of 100 participants show clear overlap, also in more detailed areas of the image. You can clearly recognize detailed patterns (menu items, buttons, images, etc). With about 100 participants you can better quantify your results.
These two random samples with 200 random participants each show identical results.
Trends versus Quantification
The number of participants you should use for a reliable test really depends on the purpose of your test and the type of questions you’re asking. As a rule of thumb you’re already able to measure basic trends with smaller data sets. If you’re looking for quantifiable data you should be focusing on more participants.
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