 I'm having a moment, folks. I happen to read a prompt that didn't agree with me. So I'm all like, inside right now, parametric analysis. So this particular example of parametric analysis is going to refer to what we don't do in behave analysis. So parametric analysis is a tool that you use in statistics when your data is normally distributed. So when your data is normally distributed, we can draw some conclusions about your control group versus your experimental group. But how far the data are pushed apart from it, or each group is pushed apart, relative to this parametric curve, right? So the bell curve, right? So when we plot all our data on one graph on a histogram, plot our other group's data on another histogram, ultimately with parametric analysis, what you're doing is finding out how much they overlap or how much they don't overlap. T tests, ANOVA's, ANCOVA's, all of those OVA things, right? All of those are tools that you use to analyze parametric data. And you do parametric analysis, right? And then we do this type of inferential statistics. We're not going to be doing that in behave analysis very often.