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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:

enter image description here enter image description here

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.

bugsyb
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    Re the edit: I don't believe you could have done justice to the many detailed answers at that thread in the 12 minutes that elapsed between the time I pointed out the duplicate and the time you completed your edit. The answers are there. I am optimistic that spending more time and attention on them will reward your curiosity and answer the questions you have. – whuber Jun 30 '17 at 20:38

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