Many designers are self-taught, intuitive consumers of research who can translate insights into great designs. But few are trained in the arcane art of research itself. For that reason, many designers don’t know the finer differences between qual and quant research and end up using their respective results inappropriately.
Quantitative research is based on the assumption that random events are predictable, and if you compare your results to pure random results, you can discern distinctive, meaningful patterns about the social world.
Random events are relatively MORE predictable if you have more of them. Imagine if you flipped a coin 20 times. How many heads would you get? Now if you flipped it 20,000 times? You’re more likely to get an even 50/50 split — which is what most people would predict. If you got a 65/35 split with 2o flips, okay, could happen. But with 20,000 flips? No way. Something else is going on.
Translate that to design research by looking at gender, for example. Let’s say you have 20 people, 10 men and 10 women. 65% of the women choose one design, while only 35% of the men do. Is this a meaningful pattern? Impossible to say — you only have 20 people. Now if you had 200 people (100 men and 100 women) and 65 of the women chose one design, chances are you have a meaningful pattern.
This is why sample size matters in quantitative research. But, little known fact, sample size is COMPLETELY IRRELEVANT in qualitative research. Why?
Qualitative research assumes that people have meaningful experiences that can be interpreted. Notice how there’s nothing in there about “prediction” or “randomness.” People have experiences. Researchers discern what these experiences signify. That’s it. Sample size is not only irrelevant, it actually gets in the way of important insight.
Consider the case study, for example. Few people would say case studies are useless. We can learn a great deal about a single design case, where it went wrong and where it went right. The problem comes when you try to predict future events based on this single event.
If you abandon the need for prediction, then sample size never matters. You can always derive insight about design problems from even a single case. Designers that attempt to predict “success” of a single design change, for example, should test that change, repeatedly, with a probability sample.