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Technical Perspective: Does Your Experiment Smell?


dog's eyes and nose

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Online human-behavior experimentation is pervasive, manifold, and unavoidable. Leading digital companies routinely conduct over 1,000 A/B tests every month with millions of users. Online labor markets boast hundreds of thousands of workers to hire for crowdsourcing tasks, including experimentation and beta-testing. Outside industry, academic researchers utilize online labor markets to run behavioral experiments that span from cooperation games to protein folding tasks.

Hidden behind a deceiving façade of simplicity, implementing a human-behavior experiment for unbiased statistical inference is a task not to be taken lightly. It requires knowledge of computer programming, statistical inference, experimental design, and even behavioral insights. This unique mix of skills is generally honed with practice, heart-breaking mistakes, and "code smells." Popularized by Martin Fowler's book, Refactoring: Improving the Design of Existing Code, code smells indicate certain code structures that violate fundamental design principles and increase the risk of unintended software behavior. Code smells that lead to failures of randomization—the process of assigning observation units (users, devices, and so on) to treatments—are a threat to the validity of experiments. For instance, a probability incorrectly set may bar users to enter a particular treatment, or a degraded user experience in one treatment might lead to a higher attrition rate (that is, dropouts).


 

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