What are the differences between bootstrapping and parametric methods?

What are the differences between bootstrapping and parametric methods?

Bootstrapping is a resampling technique used to estimate statistics on a dataset by sampling with replacement, while parametric methods assume a specific distribution for the data and use parameters to summarize it.

Can you give me an example of when to use bootstrapping?

Sure! Bootstrapping is useful when you have a small sample size and want to estimate the confidence intervals for a statistic, like the mean.

What are the advantages of using parametric methods?

Parametric methods are often more powerful and efficient when the assumptions about the data distribution are met, leading to more precise estimates.

Are there any drawbacks to using bootstrapping?

Yes, bootstrapping can be computationally intensive and may not perform well if the sample size is too small or if the data has strong dependencies.

How do I choose between bootstrapping and parametric methods?

Consider the sample size, the underlying distribution of your data, and the specific analysis goals. If assumptions can be met, parametric methods may be preferable.