In quantitative research, we’re always comparing things, that’s what quantitative data, numerical data is really good for. We also need to make meaningful comparisons in order to make sense of that quantitative data.

Often we use numerical data to compare two or more designs. Let’s say that we’re comparing our products, user experience to our competitors. When we compare the two sets of quantitative data for these products, we want to know if that result is reliable. That’s what statistical significance can tell us. If the difference between two numbers is statistically significant, it’s reliable. The result that we’re seeing probably isn’t due to random chance. Another way to think about this, if we ran the study in the same way a second time, we’d expect to see a similar result. So if we run a quantitative use research study and the data suggests that we have a better use than our competitor, we want to know if that’s a reliable finding. We can use statistics formulas to calculate the statistical significance for the relationship between these two data sets. If the formula says that we have statistical significance, that means it’s reliable. From a statistics standpoint, at least we can probably trust that if we’ve ran the study again in the same way, we’d again find that we’re better than our competitor. But if we don’t find statistical significance, there’s some risk that our finding isn’t really there.

Maybe if we ran the study again, we’d find that actually our competitors UCS looks better than ours. Here’s one thing that sometimes confuses people. Statistical significance refers only to comparisons. You wouldn’t determine statistical significance for a single set of data. Let’s imagine that I run a quantitative usability test and I find that my users on average took three minutes to complete a task. I wouldn’t be able to say that three minutes is statistically significant, but I could compare the average time on task for my product to the average time on task for my competitors product, and I can determine statistical significance for that relationship between the two products.

Quantitative data is often used by companies to make decisions. Should we approve funding for a new redesign project? Where should we focus our redesign efforts? Which design option works best? Whenever you’re trying to interpret numbers, make sure that you ask, do we have statistical significance for this finding? Is this comparison reliable?