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Randomization in Online Experiments

Randomization in Online Experiments AbstractMost scientists consider randomized experiments to be the best method available to establish causality. On the Internet, during the past twenty-five years, randomized experiments have become common, often referred to as A/B testing. For practical reasons, much A/B testing does not use pseudo-random number generators to implement randomization. Instead, hash functions are used to transform the distribution of identifiers of experimental units into a uniform distribution. Using two large, industry data sets, I demonstrate that the success of hash-based quasi-randomization strategies depends greatly on the hash function used: MD5 yielded good results, while SHA512 yielded less impressive ones. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Jahrbücher für Nationalökonomie und Statistik de Gruyter

Randomization in Online Experiments

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Publisher
de Gruyter
Copyright
© 2018 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston
ISSN
0021-4027
eISSN
0021-4027
DOI
10.1515/jbnst-2018-0006
Publisher site
See Article on Publisher Site

Abstract

AbstractMost scientists consider randomized experiments to be the best method available to establish causality. On the Internet, during the past twenty-five years, randomized experiments have become common, often referred to as A/B testing. For practical reasons, much A/B testing does not use pseudo-random number generators to implement randomization. Instead, hash functions are used to transform the distribution of identifiers of experimental units into a uniform distribution. Using two large, industry data sets, I demonstrate that the success of hash-based quasi-randomization strategies depends greatly on the hash function used: MD5 yielded good results, while SHA512 yielded less impressive ones.

Journal

Jahrbücher für Nationalökonomie und Statistikde Gruyter

Published: Jul 26, 2018

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