  Dataset replication is a useful tool for assessing whether improvements in
test accuracy on a specific benchmark correspond to improvements in models'
ability to generalize reliably. In this work, we present unintuitive yet
significant ways in which standard approaches to dataset replication introduce
statistical bias, skewing the resulting observations. We study ImageNet-v2, a
replication of the ImageNet dataset on which models exhibit a significant
(11-14%) drop in accuracy, even after controlling for a standard
human-in-the-loop measure of data quality. We show that after correcting for
the identified statistical bias, only an estimated $3.6\% \pm 1.5\%$ of the
original $11.7\% \pm 1.0\%$ accuracy drop remains unaccounted for. We conclude
with concrete recommendations for recognizing and avoiding bias in dataset
replication. Code for our study is publicly available at
http://github.com/MadryLab/dataset-replication-analysis .
