A/B testing is a hypothesis testing method that compares the results of two experiment configurations. In many cases, a control is compared against an experiment. It's popularly used by websites to test content and configurations with web traffic to optimize metrics such as click-through and conversion rates. On a high traffic site, large numbers of experiments can be run to support continual optimization.
A hypothesis test that compares two alternatives, often an experiment and a control.
Making decisions and solving problems with data as opposed to guesses.
A focus on low level optimization can cause a firm to miss big-picture opportunities.
split testing, bucket testing
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