  Adaptive data analysis has posed a challenge to science due to its ability to
generate false hypotheses on moderately large data sets. In general, with
non-adaptive data analyses (where queries to the data are generated without
being influenced by answers to previous queries) a data set containing $n$
samples may support exponentially many queries in $n$. This number reduces to
linearly many under naive adaptive data analysis, and even sophisticated
remedies such as the Reusable Holdout (Dwork et. al 2015) only allow
quadratically many queries in $n$.
  In this work, we propose a new framework for adaptive science which
exponentially improves on this number of queries under a restricted yet
scientifically relevant setting, where the goal of the scientist is to find a
single (or a few) true hypotheses about the universe based on the samples. Such
a setting may describe the search for predictive factors of some disease based
on medical data, where the analyst may wish to try a number of predictive
models until a satisfactory one is found.
  Our solution, the Generic Holdout, involves two simple ingredients: (1) a
partitioning of the data into a exploration set and a holdout set and (2) a
limited exposure strategy for the holdout set. An analyst is free to use the
exploration set arbitrarily, but when testing hypotheses against the holdout
set, the analyst only learns the answer to the question: "Is the given
hypothesis true (empirically) on the holdout set?" -- and no more information,
such as "how well" the hypothesis fits the holdout set. The resulting scheme is
immediate to analyze, but despite its simplicity we do not believe our method
is obvious, as evidenced by the many violations in practice.
  Our proposal can be seen as an alternative to pre-registration, and allows
researchers to get the benefits of adaptive data analysis without the problems
of adaptivity.
