Approximate 95% confidence intervals for an unknown parameter are typically of the form (t - 1.96*se, t + 1.96*se), where t is the point estimate and "se" is its estimated standard error. Here a tacit assumption is being made: that the true standard error
of t is constant, or nearly so, across the interval. Bootstrap methods now allow us to assess not only se but also its rate of change. The talk describes such methods and how they can be incorporated into more accurate approximate confidence intervals.