In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

No abstract

Companies need to know how much value their ideas deliver to customers. One of the most powerful ways to accurately measure this is by conducting online controlled experiments (OCEs). To run experiments, however, companies need to develop strong experimentation practices as well as align their organization and culture to experimentation. The main objective of this paper is to demonstrate how to run OCEs at large scale using the experience of companies that succeeded in scaling. Based on case study research at Microsoft, Booking.com, Skyscanner, and Intuit, we present our main contribution—The Experiment Growth Model. This four‐stage model addresses the seven critical aspects of experimentation and can help companies to transform their organizations into learning laboratories where new ideas can be tested with scientific accuracy. Ultimately, this should lead to better products and services.

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of scientists from a wide range of backgrounds working on data science tasks by allowing them to make direct code contributions in the languages used by them (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services.In this paper, we provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of data scientists from a wide range of backgrounds by allowing them to make direct code contributions in the languages used by scientists (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services.In this paper, we utilize a case-study research method to provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.