Suppose I have a simple linear regression problem, and I use the bootstrap to obtain a 95% confidence interval for the slope of the regression line. (Maybe I will use this confidence interval to test whether I can reject the null hypothesis that the slope is 0.)
Suppose that the data has heteroskedasticity. I know that when I see heteroskedasticity, I should potentially worry, as this violates the assumptions behind linear regression and a linear model. For example, I know that standard errors for the parameters of the linear model computed using standard methods (linear algebra, least squares, etc.) might be wrong or misleading.
What about when I use the bootstrap, instead of standard methods? Does that avoid those problems? Do I get good 95% confidence intervals for the slope, even in the presence of heteroskedasticity? Or are there caveats and cautions that I need to be wary of?