 Chapter five is called Z scores or also called standard scores. And while it may seem like a really weird topic, it's something that has practical implication. So in terms of what we're talking about this score, it's a way of taking individual scores within a larger distribution and translating them from their original metric, which might be minutes or miles or dollars into a standard score. What that means is a score for the distribution that has a mean of zero and a standard deviation of one. And then you get a score that tells you how many standard deviations that score is from the mean. Now that may not sound really important, but it becomes critical when you're trying to compare scores from different distributions. And that's why we're learning this. So if you want to compare somebody who takes the SAT with somebody who takes the ACT, as an example I use in the chapter, this is a good way to do it. A more practical situation might be trying to compare cost of living in different cities. So for instance, we're here in Orham, Utah. It's cheaper to live here than it is to live in San Francisco or New York City. And so if you're comparing income, income is higher in those places and it's lower in Orham, but you want to compare that to the cost of living. And that's a little bit like standardizing. Now it turns out that San Francisco costs twice as much to live in as most cities in Utah. So you better be making over twice as much to justify it from a totally financial perspective. But that's one way of translating between things. Another way is if you're for instance doing a social media campaign for a business, but you're using different platforms. Say for instance, you're using Instagram and you're doing something on TikTok. You have different numbers of users and so you want to compensate for those different number of users in part by looking at the relative reach of each of these different campaigns. And Z scores are an analogous way of comparing scores from different distributions. And so again, it can both put something into a more meaningful metric for you and it can help you make meaningful comparisons for scores that come from different distributions. That leads to practical decision making.