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| - | ====== ab test ====== | + | ====== A/B test ====== |
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| + | AB 테스트 추천 도서 : [[https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264|Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing]] [[https://coupa.ng/b7Omnt|쿠팡]] | ||
| + | 통계적 지식 뿐 아니라 실제 적용에서 고민해야할 문제들에 대한 상세한 설명이 있다. | ||
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| + | [[data_analysis:convergence_testing]] | ||
| https://reflectivedata.com/comprehensive-guide-to-statistics-in-a-b-testing/ | https://reflectivedata.com/comprehensive-guide-to-statistics-in-a-b-testing/ | ||
| - | https://cdn2.hubspot.net/hubfs/310840/VWO_SmartStats_technical_whitepaper.pdf | ||
| https://www.slideshare.net/cojette/ab-150118831 | https://www.slideshare.net/cojette/ab-150118831 | ||
| https://onlinemix.tistory.com/entry/significant-result-from-ab-testing | https://onlinemix.tistory.com/entry/significant-result-from-ab-testing | ||
| - | https://is.muni.cz/th/wt0tu/Humaj-thesis.pdf | ||
| https://cxl.com/blog/ab-testing-guide/ | https://cxl.com/blog/ab-testing-guide/ | ||
| https://hbr.org/2017/06/a-refresher-on-ab-testing | https://hbr.org/2017/06/a-refresher-on-ab-testing | ||
| + | Bayesian | ||
| + | https://is.muni.cz/th/wt0tu/Humaj-thesis.pdf | ||
| + | https://brunch.co.kr/@gimmesilver/15 | ||
| + | [[https://cdn2.hubspot.net/hubfs/310840/VWO_SmartStats_technical_whitepaper.pdf|VWO_SmartStats_technical_whitepaper]] | ||
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| + | [[https://www.researchgate.net/publication/287927836_Sample_Size_Calculation_for_Two_Independent_Groups_A_Useful_Rule_of_Thumb|Sample size calculation for two independent Groups]] | ||
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| + | https://www.evanmiller.org/ab-testing/ | ||
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| + | MAB | ||
| + | http://sanghyukchun.github.io/96/ | ||
| + | https://kw94.tistory.com/49 | ||
| + | https://sumniya.tistory.com/9 | ||
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| + | ===== 실행순서 ==== | ||
| + | * 가설 : 인터뷰, idea, 기존자료 분석 | ||
| + | * 실험설계 : control/treatment, 지표, bayesian/frequentist | ||
| + | * Random unit : user/session/page | ||
| + | * Target unit : 전체 / segment | ||
| + | * Size : sample, traffic 분배 | ||
| + | * How long : 학습효과 | ||
| + | * 실행 : 모니터링. 오류, 다른 feature들이 떨어지는지, 예상치 못하게 효율이 떨어지는지 (cf. 가드레일 metric) | ||
| + | * 분석 : traffic 분배 정합성, 샘플수, 다른 segments의 특성, 다른 지표, Bot은 없는지 | ||
| + | * 결정 : 통계적vs실질적 유의도, A/A test->검증 | ||
| {{tag>data_analysis 실험 experiment ABtest}} | {{tag>data_analysis 실험 experiment ABtest}} | ||
