| 000 | 02304 a2200241 4500 | ||
|---|---|---|---|
| 008 | 240531b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9781108724265 | ||
| 082 | _a302.231 KOH | ||
| 100 | _aKohavi, Ron | ||
| 245 | _aTrustworthy online controlled experiments: a practical guide to A/B testing | ||
| 260 |
_aNew York: _bCambridge University Press, _c2023. |
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| 300 |
_axviii, 271p.: _bill.; pbk.: _c22cm. |
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| 520 | _aGetting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice. Dives into over twenty advanced topics, including ramp-up procedures, computations of standard-deviations, and instrumentation issues Addresses practical issues not covered in theoretical statistics, such as violations of independent assumptions Additional online material is featured on the book's website https://www.cambridge.org/in/universitypress/subjects/computer-science/knowledge-management-databases-and-data-mining/trustworthy-online-controlled-experiments-practical-guide-b-testing?format=PB | ||
| 650 | _aStatistics | ||
| 650 | _aGuardrail Metrics | ||
| 650 | _aInstrumentation | ||
| 650 | _aScaling Experiment Analyses | ||
| 650 | _aOverall Evaluation Criterion | ||
| 650 | _aVariance Estimation | ||
| 700 |
_aTang, Diane _eCo-author |
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| 700 |
_aXu, Ya _eCo-author |
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| 942 |
_cTD _2ddc |
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| 999 |
_c60377 _d60377 |
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