Intro to statistics with R - Measures of Variability

So to review this entire lecture, the point of summary statistics is we can't always show our audience or when we're writing thing writing up research for a publication in a journal or an article in a newspaper or on a website, we can't show all those histograms for all our variables. It just becomes too much information for the audience to digest. So we often want to just give some talking points, some summary statistics. And the most common summary statistics are measures of central tendency. And the most common one of those is the mean or average. But you might want to do median in the case of extremely skewed distributions. But it's also really important as you start getting into statistics and really important for the rest of this course to think beyond just the mean or measures of central tendency and think about measures of variability. Variance is going to be a really important concept as we move forward. So it's important that you understand how to calculate standard deviation and variance. And it's important that you understand conceptually what standard deviation and variance mean and what they convey. Finally, it's important that you know the following formulas and that you could just do these by hand. Of course, we're going to do all our calculations using the R software. It's less error prone and a lot faster and convenient. Right. But you should just know how to do these off the top of your head. So again, hopefully this one was review. If it's not, just give yourself some practice on it. So the mean is just the sum of all the scores divided by the number of scores. And then I gave you this one formula for standard deviation and variance. This first one is used for descriptive statistics. And that's still all we're dealing with is descriptive statistics. We're just describing what's going on in a sample.