  Traditional statistical analysis requires that the analysis process and data
are independent. By contrast, the new field of adaptive data analysis hopes to
understand and provide algorithms and accuracy guarantees for research as it is
commonly performed in practice, as an iterative process of interacting
repeatedly with the same data set, such as repeated tests against a holdout
set. Previous work has defined a model with a rather strong lower bound on
sample complexity in terms of the number of queries, $n\sim\sqrt q$, arguing
that adaptive data analysis is much harder than static data analysis, where
$n\sim\log q$ is possible. Instead, we argue that those strong lower bounds
point to a limitation of the previous model in that it must consider wildly
asymmetric scenarios which do not hold in typical applications.
  To better understand other difficulties of adaptivity, we propose a new
Bayesian version of the problem that mandates symmetry. Since the other lower
bound techniques are ruled out, we can more effectively see difficulties that
might otherwise be overshadowed. As a first contribution to this model, we
produce a new problem using error-correcting codes on which a large family of
methods, including all previously proposed algorithms, require roughly
$n\sim\sqrt[4]q$. These early results illustrate new difficulties in adaptive
data analysis regarding slightly correlated queries on problems with
concentrated uncertainty.
