Interview Question: Save Thyself!

Sample Question #86 (statistics – applied methods)

What is bootstrapping in statistics? Why is it used?

(Comment: again, I want to emphasize that you do not need to know the answer to every question posted on this blog — I simply try to post a wide variety of topics so every reader finds something he or she feels worthy of working on. Even though these are real interview questions, if the interviewer doesn’t think you know a certain subject, such as bootstrap methods, they won’t ask you about it.)

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One Response to Interview Question: Save Thyself!

  1. Brett says:

    In statistics, bootstrapping refers to a collection of computationally intensive techniques for inferring a statistical estimator’s distribution (esp. its mean and variance) through a concept known as resampling, which involves repeatedly computing inference statistics from the data sample, with replacement. Some people confuse bootstrapping with Monte Carlo simulation. In theory, these two are different techniques, and bootstrapping does not require MC simulation, although in practice, bootstrapping is often used in conjunction with MC. The reason bootstrapping is useful is, when trying to derive the distribution of the estimator, we rarely know the true distribution of the sample itself, so we have to use the empirical sample distribution to infer the estimator’s own distribution. Bootstrapping gives us a computational method to go from data sample to estimator distribution. Sounds complicated? It is, but you should be able to see how bootstrapping relates to "common" estimation techniques such as MLE and least squares. 

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