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A/B testing, a data science perspective : an introduction to data and statistics for improved U/X /
"Deciding whether or not to launch a new product or feature is a resource management bet for any Internet business. Conducting rigorous online A/B tests flattens the risk. Drawing on her experience at Airbnb, data scientist Lisa Qian offers a practical ten-step guide to designing and executing...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "Deciding whether or not to launch a new product or feature is a resource management bet for any Internet business. Conducting rigorous online A/B tests flattens the risk. Drawing on her experience at Airbnb, data scientist Lisa Qian offers a practical ten-step guide to designing and executing statistically sound A/B tests. Discover best practices for defining test goals and hypotheses; Learn to identify controls, treatments, key metrics, and data collection needs; Understand the role of appropriate logging in data collection; Determine how to frame your tests (size of difference detection, visitor sample size, etc.); Master the importance of testing for systematic biases; Run power tests to determine how much data to collect; Learn how experimenting on logged out users can introduce bias; Understand when cannibalization is an issue and how to deal with it; Review accepted A/B testing tools (Google Analytics, Vanity, Unbounce, among others)."--Resource description page. |
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| Notas: | Title from title screen (viewed October 20, 2015). Date of publication from resource description page. |
| Descripción Física: | 1 online resource (1 streaming video file (1 hr., 16 min., 49 sec.)) : digital, sound, color |