By Ron Kohavi and Roger Longbotham
To appear in the Encyclopedia of Machine Learning and Data Mining, edited by Claude Sammut and Geoff Webb
© 2017. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version is published in the Encyclopedia of Machine Learning and Data Mining.
The internet connectivity of client software (e.g., apps running on phones and PCs), web sites, and online services provide an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called A/B tests, split tests, randomized experiments, control/treatment tests, and online field experiments. Unlike most data mining techniques for finding correlational patterns, controlled experiments allow establishing a causal relationship with high probability. Experimenters can utilize the Scientific Method to form a hypothesis of the form “If a specific change is introduced, will it improve key metrics?” and evaluate it with real users.
The theory of a controlled experiment dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, and the topic of offline experiments is well developed in Statistics (Box 2005). Online Controlled Experiments started to be used in the late 1990s with the growth of the Internet. Today, many large sites, including Amazon, Bing, Facebook, Google, LinkedIn, and Yahoo! run thousands to tens of thousands of experiments each year testing user interface (UI) changes, enhancements to algorithms (search, ads, personalization, recommendation, etc.), changes to apps, content management system, etc. Online controlled experiments are now considered an indispensable tool, and their use is growing for startups and smaller websites. Controlled experiments are especially useful in combination with Agile software development (Martin 2008, Rubin 2012), Steve Blank’s Customer Development process (Blank 2005), and MVPs (Minimum Viable Products) popularized by Eric Ries’s Lean Startup (Ries 2011).
ACMRef: Ron Kohavi and Roger Longbotham, 2017. Online Controlled Experiments and A/B Tests. In Encyclopedia of Machine Learning and Data Mining, Claude Sammut and Geoff Webb (editors). ISBN: 978-1-4899-7502-7. http://www.springer.com/us/book/9781489976857