What are the key differences between bootstrapping and Bayesian methods?

What are the key differences between bootstrapping and Bayesian methods?

Bootstrapping is a resampling technique used to estimate statistics on a dataset, while Bayesian methods incorporate prior knowledge and update beliefs based on new evidence.

Can you explain how bootstrapping works in more detail?

Sure! Bootstrapping involves repeatedly sampling from a dataset with replacement to create 'bootstrap samples' and then calculating the desired statistic for each sample.

What about Bayesian methods? How do they update beliefs?

Bayesian methods use Bayes' theorem to update the probability of a hypothesis as more evidence becomes available, combining prior beliefs with new data.

Which method is better for small sample sizes?

Bootstrapping can be more effective for small sample sizes as it allows for estimating the sampling distribution without relying heavily on assumptions.