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I am finding it difficult to understand the concept of Bootstrapping in statistics  . I know what sampling is , that is , taking a 'sample_size' number of observations from a population to estimate some of that population statistics like mean  , SD etc  . I thought bootstrapping was doing that same process of sampling multiple times  , but it doesn't look like that's a proper way to put it  . Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused  .

Could someone please explain the difference between the two in simple and intuitive manner  ? i.e , what exactly is each one of them doing  ?

I am finding it difficult to understand the concept of Bootstrapping in statistics. I know what sampling is: taking a sample_size number of observations from a population to estimate some population statistic like mean, SD, etc. I thought bootstrapping was doing that same process of sampling multiple times, but it doesn't look like that's a proper way to put it. Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused.

Could someone please explain the difference between the two in a simple and intuitive manner? What exactly is each one of them doing?

I am finding it difficult to understand the concept of Bootstrapping in statistics . I know what sampling is , that is , taking a 'sample_size' number of observations from a population to estimate some of that population statistics like mean , SD etc . I thought bootstrapping was doing that same process of sampling multiple times , but it doesn't look like that's a proper way to put it . Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused .

Pages 187-190 of the book " Introduction to Statistical Learning " http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf

These pages explain Bootstrap method by first using simulated datasets( I'm not sure what is meant by that) from original population to estimate 'alpha' . But This approach does not seem to be applicable in practical scenarios. So , we use Bootstrap samples instead . Bootstrap samples they say are taken from ' Orginal data set'

I really don't understand these terminologies and what the authors mean by the terms 'Original population' , 'Original data set' and 'simulated data set' to explain Bootstrap .

Could someone please explain to me what those 3 pages(187-190) mean by these terms and hence what do they conclude of bootstrap vs Ordinary sampling ?

I am finding it difficult to understand the concept of Bootstrapping in statistics . I know what sampling is , that is , taking a 'sample_size' number of observations from a population to estimate some of that population statistics like mean , SD etc . I thought bootstrapping was doing that same process of sampling multiple times , but it doesn't look like that's a proper way to put it . Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused .

Could someone please explain the difference between the two in simple and intuitive manner ? i.e , what exactly is each one of them doing ?

I am finding it difficult to understand the concept of Bootstrapping in statistics . I know what sampling is , that is , taking a 'sample_size' number of observations from a population to estimate some of that population statistics like mean , SD etc . I thought bootstrapping was doing that same process of sampling multiple times , but it doesn't look like that's a proper way to put it . Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused .

Could someone please explain the difference between the two in simple and intuitive manner ? i.e , what exactly is each one of them doing ?

I am finding it difficult to understand the concept of Bootstrapping in statistics . I know what sampling is , that is , taking a 'sample_size' number of observations from a population to estimate some of that population statistics like mean , SD etc . I thought bootstrapping was doing that same process of sampling multiple times , but it doesn't look like that's a proper way to put it . Some sources say bootstrapping takes a number of samples with size equal to the original dataset while some others say it takes samples of desired sample size from within a bigger sample of a dataset. All of these definitions got me confused .

Pages 187-190 of the book " Introduction to Statistical Learning " http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf

These pages explain Bootstrap method by first using simulated datasets( I'm not sure what is meant by that) from original population to estimate 'alpha' . But This approach does not seem to be applicable in practical scenarios. So , we use Bootstrap samples instead . Bootstrap samples they say are taken from ' Orginal data set'

I really don't understand these terminologies and what the authors mean by the terms 'Original population' , 'Original data set' and 'simulated data set' to explain Bootstrap .

Could someone please explain to me what those 3 pages(187-190) mean by these terms and hence what do they conclude of bootstrap vs Ordinary sampling ?