probablity

Hey Wagal,   I'll quickly explain the general concept of the section.  So, there are theoretical distributions we know a lot about - Normal, Poisson, Exponential, etc. We know what equations define them  - PDFs - and what equations represent their cumulative properties - CDFs. We use these equations to estimate the likelihood that a given variable which follows such a distribution would fall within some interval (from a to b). Based on this likelihood, we can test whether something is out of the ordinary. This last part is called hypothesis testing and we explain it in detail in the Statistics course.    Basically, any dataset we have can closely resemble one of the distributions we know a lot about. Of course, we can calculate the mean and variance of our data ourselves and then, based on them, find the PDF and CDF equations. (We aren't showing that part because it requires calculus.)    Essentially, the idea behind these frequency distributions is to tell us how likely/unlikely it is to get a specific value for X, knowing what values X usually gets. To avoid an overlapping with statistics or leaning into proof-writing, in this section we're simply noting down some useful details you should know about common distributions. Additionally, we're letting you know scenarios in which different distributions are used, so when you get data about a random variable, you can determine which distribution it resembles.    Best, 365 Vik