# About confidence/prediction intervals: parametric methods VS non-parametric (via bootstrap) methods

About the methodology to find confidence and/or prediction intervals in, let's say, a regression problem, I know 2 main options:

1. Checking normality in the estimates/predictions distribution, and applying well known Gaussian alike methods to find those intervals if the distribution is gaussian
2. Applying non-parametric methodologies like bootstraping, so we do not need to assume/check/care whether our distribution is normal

With this in mind, I would basically always go for the second one because:

• it is meant to be generic, as it does not assume any kind of distribution
• it feels more like experimental as you can freely run as many iterations as you want (well, if it is computionally feasible)

The only drawback I could see is the computational cost, but it could be parallelized...