Facebook is continuing their mission of donating goodness to the community with the release of sourcr code called PlanOut. PlanOut is used for online A\/B testing experiments.<\/p>\n
This is a great move for Facebook, developers, and the data community. As we talk about on SiliconANGLE Wikibon and theCUBE is that Data science is the hottest emerging trend that is creating new value that has never been seen before.<\/p>\n
This is a great preview of the upcoming F8 conference on April 30th in SF.<\/p>\n
When people think about the tools of data science, they often focus on machine learning, statistics, and data manipulation. Modeling massive datasets is indispensable for making predictions like predicting which set of News Feed stories or search results are most relevant to people. But such models also have limitations in terms of their ability to help with understanding cause-and-effect relationships that lead to building better products and to advancing behavioral science. <\/p>\n
Data science needs better tools for running experiments <\/p>\n
Despite the abundance of experimental practices in the Internet industry, there are few tools or standard practices for running online field experiments. And existing tools tend to focus on rolling out new features, or automatically optimizing some outcome of interest. <\/p>\n
At Facebook, we run over a thousand experiments each day. While many of these experiments are designed to optimize specific outcomes, others aim to inform long-term design decisions. And because we run so many experiments, we need reliable ways of routinizing experimentation. As Ronald Fisher, a pioneer in statistics and experimental design said, To consult the statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of. Many online experiments are implemented by engineers who are not trained statisticians. While experiments are often simple to analyze when done correctly, it can be surprisingly easy to make mistakes in their design, implementation, logging, and analysis. One way to consult a statistician in advance is to have their advice built into tools for running experiments <\/p>\n
PlanOut: a framework for running online field experiments <\/p>\n
Good tools not only enable good practices, they encourage them. Thats why we created PlanOut, a set of tools for running online field experiments, and are sharing an open source version of it as part of the Data Science Teams first software release. <\/p>\n
Importantly, PlanOut gives engineers and scientists a language for defining random assignment procedures. Experiments, ranging from simple A\/B tests, to factorial designs that decompose large interface changes, to more complex within-subjects designs, can be expressed with only a few lines of code. In this way, PlanOut encourages running experiments that are more akin to the kind you see in the behavioral sciences. <\/p>\n
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\nFacebook Data Science Team Announces Open Source Tools for A\/B Testing<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"