  A pervasive issue in statistical hypothesis testing is that the reported
$p$-values are biased downward by data "peeking" -- the practice of reporting
only progressively extreme values of the test statistic as more data samples
are collected. We develop principled mechanisms to estimate such running
extrema of test statistics, which directly address the effect of peeking in
some general scenarios.
