This question already has an answer here:
So I'm trying to understand bootstrapping,
I watched the following video: https://www.youtube.com/watch?v=gcPIyeqymOU&t=338s
And starting from 2:53 the speaker explains that through bootstrapping we can get a closer inference on the population mean by creating a sample distribution of random samples from our original sample
I tried to visualize this in python with the following code:
However, as you can see, the if I make a sampling distribution of my sample I get a distribution that's centered around the sample mean, not the population mean.
This actually makes sense to me, but because of this result I don't understand the purpose of bootstrapping. Could someone explain how bootstrapping works?
EDIT: I read the answer here: Explaining to laypeople why bootstrapping works
And it still doesn't make sense to me based on the way that i've visualized it. To me, it seems that I'm only getting information about the sample, not the population.