  We design a general framework for answering adaptive statistical queries that
focuses on providing explicit confidence intervals along with point estimates.
Prior work in this area has either focused on providing tight confidence
intervals for specific analyses, or providing general worst-case bounds for
point estimates. Unfortunately, as we observe, these worst-case bounds are
loose in many settings --- often not even beating simple baselines like sample
splitting. Our main contribution is to design a framework for providing valid,
instance-specific confidence intervals for point estimates that can be
generated by heuristics. When paired with good heuristics, this method gives
guarantees that are orders of magnitude better than the best worst-case bounds.
We provide a Python library implementing our method.
