![]() ![]() In fact, power is simply one minus the false negative rate. Continuing on an example from Part 3, a false negative corresponds to labeling the photo of the cat as a “not cat.” False negatives are closely related to the statistical concept of power, which gives the probability of a true positive given the experimental design and a true effect of a specific size. False negatives and powerĪ false negative occurs when the data do not indicate a meaningful difference between treatment and control, but in truth there is a difference. In this post, we’ll do the same for false negatives and the related concept of statistical power.įigure 1: As in Part 3, we’ll use thought exercises based on flipping coins, such as this one displaying Caesar Augustus, to build up intuition about core statistical concepts. We then used simple thought exercises based on flipping coins to build intuition around false positives and related concepts such as statistical significance, p-values, and confidence intervals. In Part 3: False positives and statistical significance, we defined the two types of mistakes that can occur when interpreting test results: false positives and false negatives. Subsequent posts will go into more details on experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix. ![]() Need to catch up? Have a look at Part 1 (Decision Making at Netflix), Part 2 (What is an A/B Test?), Part 3 (False positives and statistical significance). ![]() This is the fourth post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, and Colin McFarland Interpreting A/B test results: false negatives and power ![]()
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