Distribution fitting

Okay kiddo, so imagine you have a big jar of candy. You want to know how many red candies are in the jar, but you don't want to count every single one because that would be too hard. So instead, you take a small sample of candies from the jar and look at how many of them are red.

Now, you might wonder if the number of red candies in the sample is the same as the number of red candies in the whole jar. That's where distribution fitting comes in!

Distribution fitting is when we try to figure out what kind of distribution or pattern the candy colors (or any other data) in the jar follow. We use math to do this, and there are lots of different distribution patterns we could use, kind of like different puzzle shapes we could fit the candy data into. Each pattern has its own special math formula that helps us understand the data better.

Once we've figured out what distribution pattern the candy follows (like maybe the number of red candies follows a "normal" pattern), we can use that pattern to make predictions about the jar as a whole! We can use the sample data we have to estimate how many red candies are likely in the whole jar. Pretty cool, right?