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The A/B testing problem with Gaussian priors

Author

Listed:
  • Azevedo, Eduardo M.
  • Mao, David
  • Montiel Olea, José Luis
  • Velez, Amilcar

Abstract

A risk-neutral firm can perform a randomized experiment (A/B test) to learn about the effects of implementing an idea of unknown quality. The firm's goal is to decide the experiment's sample size and whether or not the idea should be implemented after observing the experiment's outcome. We show that when the distribution for idea quality is Gaussian and there are linear costs of experimentation, there are exact formulae for the firm's optimal implementation decisions, the value of obtaining more data, and optimal experiment sizes. Our formulae—which assume that companies use randomized experiments to help them maximize expected profits—provide a simple alternative to i) the standard rules-of-thumb of power calculations for determining the sample size of an experiment, and also to ii) ad hoc thresholds based on statistical significance to interpret the outcome of an experiment.

Suggested Citation

  • Azevedo, Eduardo M. & Mao, David & Montiel Olea, José Luis & Velez, Amilcar, 2023. "The A/B testing problem with Gaussian priors," Journal of Economic Theory, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:jetheo:v:210:y:2023:i:c:s002205312300042x
    DOI: 10.1016/j.jet.2023.105646
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    References listed on IDEAS

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    Cited by:

    1. Yuchen Hu & Henry Zhu & Emma Brunskill & Stefan Wager, 2024. "Minimax-Regret Sample Selection in Randomized Experiments," Papers 2403.01386, arXiv.org.

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    More about this item

    Keywords

    Statistical Decision Theory; Optimal learning; Experiment design; A/B testing;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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