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Randomization in Online Experiments

Author

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  • Golyaev Konstantin

    (Konstantin Golyaev, Microsoft Research, One Microsoft Way, Redmond, WA, USA)

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.

Suggested Citation

  • Golyaev Konstantin, 2018. "Randomization in Online Experiments," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(3-4), pages 223-241, July.
    • Handle: 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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    Keywords

    Big Data; data science; Internet randomized experiments; A/B testing; hash functions;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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