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On-line experimentation (also known as A/B testing) has become an integral part of software development. To timely incorporate user feedback and continuously improve products, many software companies have adopted the culture of agile…

Applications · Statistics 2019-08-13 Yu Wang , Somit Gupta , Jiannan Lu , Ali Mahmoudzadeh , Sophia Liu

During the last few decades, online controlled experiments (also known as A/B tests) have been adopted as a golden standard for measuring business improvements in industry. In our company, there are more than a billion users participating…

Applications · Statistics 2021-08-06 Tao Xiong , Yihan Bao , Penglei Zhao , Yong Wang

Online controlled experiments are the primary tool for measuring the causal impact of product changes in digital businesses. It is increasingly common for digital products and services to interact with customers in a personalised way. Using…

Methodology · Statistics 2021-07-02 C. H. Bryan Liu , Benjamin Paul Chamberlain

Randomized experiments play a major role in data-driven decision making across many different fields and disciplines. In medicine, for example, randomized controlled trials (RCTs) are the backbone of clinical trial methodology for testing…

Applications · Statistics 2016-08-30 Andrew W. Correia

Online experiments such as Randomised Controlled Trials (RCTs) or A/B-tests are the bread and butter of modern platforms on the web. They are conducted continuously to allow platforms to estimate the causal effect of replacing system…

Machine Learning · Computer Science 2023-04-24 Olivier Jeunen

User-randomized A/B testing has emerged as the gold standard for online experimentation. However, when this kind of approach is not feasible due to legal, ethical or practical considerations, experimenters have to consider alternatives like…

Methodology · Statistics 2025-06-17 Paul Missault , Lorenzo Masoero , Christian Delbé , Thomas Richardson , Guido Imbens

A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly in…

Machine Learning · Computer Science 2022-10-18 Alexander Buchholz , Vito Bellini , Giuseppe Di Benedetto , Yannik Stein , Matteo Ruffini , Fabian Moerchen

Online controlled experiments, also known as A/B testing, are the digital equivalent of randomized controlled trials for estimating the impact of marketing campaigns on website visitors. Stratified sampling is a traditional technique for…

A/B tests are randomized experiments frequently used by companies that offer services on the Web for assessing the impact of new features. During an experiment, each user is randomly redirected to one of two versions of the website, called…

Social and Information Networks · Computer Science 2021-08-12 Francisco Galuppo Azevedo , Bruno Demattos Nogueira , Fabricio Murai , Ana Paula Couto da Silva

Online controlled experiments, such as A/B-tests, are commonly used by modern tech companies to enable continuous system improvements. Despite their paramount importance, A/B-tests are expensive: by their very definition, a percentage of…

Machine Learning · Computer Science 2024-01-09 Shubham Baweja , Neeti Pokharna , Aleksei Ustimenko , Olivier Jeunen

When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new…

Networking and Internet Architecture · Computer Science 2021-10-04 Bruce Spang , Veronica Hannan , Shravya Kunamalla , Te-Yuan Huang , Nick McKeown , Ramesh Johari

Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and learning adaptive streaming controllers. Decision-makers…

Machine Learning · Computer Science 2025-07-01 Qing Feng , Samuel Daulton , Benjamin Letham , Maximilian Balandat , Eytan Bakshy

Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning…

Applications · Statistics 2019-02-04 Timothy NeCamp , Josh Gardner , Christopher Brooks

Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small…

Methodology · Statistics 2023-06-13 Adam C. Sales , Ethan B. Prihar , Johann A. Gagnon-Bartsch , Neil T. Heffernan

A/B experiments are commonly used in research to compare the effects of changing one or more variables in two different experimental groups - a control group and a treatment group. While the benefits of using A/B experiments are widely…

Software Engineering · Computer Science 2023-09-26 Andrew Hornback , Sungeun An , Scott Bunin , Stephen Buckley , John Kos , Ashok Goel

In A/B testing two variants of a piece of software are compared in the field from an end user's point of view, enabling data-driven decision making. While widely used in practice, no comprehensive study has been conducted on the…

Software Engineering · Computer Science 2023-08-10 Federico Quin , Danny Weyns , Matthias Galster , Camila Costa Silva

Companies offering web services routinely run randomized online experiments to estimate the causal impact associated with the adoption of new features and policies on key performance metrics of interest. These experiments are used to…

Methodology · Statistics 2023-07-13 Lorenzo Masoero , Doug Hains , James McQueen

Online experimentation (or A/B testing) has been widely adopted in industry as the gold standard for measuring product impacts. Despite the wide adoption, few literatures discuss A/B testing with quantile metrics. Quantile metrics, such as…

Applications · Statistics 2019-03-22 Min Liu , Xiaohui Sun , Maneesh Varshney , Ya Xu

eBay's experimentation platform runs hundreds of A/B tests on any given day. The platform integrates with the tracking infrastructure and customer experience servers, provides the sampling service for experiments, and has the responsibility…

Applications · Statistics 2023-03-10 Keyu Nie , Zezhong Zhang , Bingquan Xu , Tao Yuan

Randomized experiments, or A/B testing, are the gold standard for evaluating interventions, yet they remain underutilized in inventory management. This study addresses this gap by analyzing A/B testing strategies in multi-item, multi-period…

Methodology · Statistics 2026-02-03 Xinqi Chen , Xingyu Bai , Zeyu Zheng , Nian Si
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