Randomization in Online Experiments

Konstantin Golyaev

Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 2018, vol. 238, issue 3-4, 223-241

Abstract: Most 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.

Keywords: Big Data; data science; Internet randomized experiments; A/B testing; hash functions (search for similar items in EconPapers)
JEL-codes: C1 C8 C9 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:jns:jbstat:v:238:y:2018:i:3-4:p:223-241:n:1

DOI: 10.1515/jbnst-2018-0006

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