Null distribution
Okay, imagine you have a big jar of candy. But you don't know how many candies are in there. You also don't know what kind of candies they are or how they taste. But you really want to find out. So you start taking samples from the jar and counting how many candies are in each sample.
Now, the null distribution is basically what you would expect to see if there were no difference between the candies in the jar. In other words, if they were all the same flavor and there were a specific number of candies in there, you would expect to see a certain pattern in the number of candies you get in each sample.
So the null distribution is like a prediction of what you would see if there were no interesting differences between the candies. It helps you figure out if any differences you do see are actually statistically significant or if they could just be due to random chance.
Think of it like this: if you took a sample of 10 candies from the jar and got 8 blue ones and 2 red ones, that sounds like a pretty big difference, right? But if the null distribution predicted that you should get 7 blue ones and 3 red ones, then maybe that difference isn't actually that significant.
So the null distribution is a way of understanding what you would expect to see by chance alone, and helps you figure out if anything you see in your data is actually meaningful or just a coincidence.
Now, the null distribution is basically what you would expect to see if there were no difference between the candies in the jar. In other words, if they were all the same flavor and there were a specific number of candies in there, you would expect to see a certain pattern in the number of candies you get in each sample.
So the null distribution is like a prediction of what you would see if there were no interesting differences between the candies. It helps you figure out if any differences you do see are actually statistically significant or if they could just be due to random chance.
Think of it like this: if you took a sample of 10 candies from the jar and got 8 blue ones and 2 red ones, that sounds like a pretty big difference, right? But if the null distribution predicted that you should get 7 blue ones and 3 red ones, then maybe that difference isn't actually that significant.
So the null distribution is a way of understanding what you would expect to see by chance alone, and helps you figure out if anything you see in your data is actually meaningful or just a coincidence.