Uniform confidence bands are widely used in empirical analysis for uncertainty qualification of nonparametric inference of unknown functions. A variety of simple implementation methods, including multiplier bootstrap, have been proposed and theoretically justified. However, an implementation over a literally continuous index set is generally computationally infeasible, and practitioners therefore compute the critical value by evaluating the statistic on a finite evaluation grid. This paper quantifies the effect of this discretization on coverage accuracy and investigates how fine the evaluation grid must be to ensure valid uniform inference. We first illustrate through simulations of a kernel-based nonparametric estimator that coarse grids can invalidate uniform inference. Surprisingly, increasing the sample size can worsen coverage on a fixed coarse grid, even as the remaining approximation error on the grid decreases. We then derive general upper bounds on the coverage probability and coverage error of uniform confidence bands based on finite-grid multiplier-bootstrap critical values. These bounds distinguish discretization error from the remaining approximation error on the grid. For local empirical processes arising from kernel smoothing, we further establish primitive conditions under which coverage converges to zero, even when the number of grid points diverges, and conditions under which asymptotically nominal coverage is attained. These results yield rate-sharp grid resolution requirements in terms of the bandwidth.

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