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Overview of Content Experiments

google.com
1The R function power.prop.test performed all the power calculations in this article.
Overview of Content Experiments - google.com - Your Highlight Location 112-113 | Added on Friday, 19 April 13 09:57:06
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Notice how the bandit is allocating observations efficiently from an economic standpoint (they?re flowing to the arms most likely to give a good return), as well as from a statistical standpoint (they?re flowing to the arms that we most want to learn about).
Overview of Content Experiments - google.com - Your Highlight Location 98-100 | Added on Friday, 19 April 13 09:53:58
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The cost savings are partially attributable to ending the experiment more quickly, and partly attributable to the experiment being less wasteful while it is running.
Overview of Content Experiments - google.com - Your Highlight Location 93-94 | Added on Friday, 19 April 13 09:51:51
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Ending an experiment based on the potential value remaining is nice because it handles ties well. For example, in an experiment with many arms, it can happen that two or more arms perform about the same, so it does not matter which is chosen. You wouldn?t want to run the experiment until you found the optimal arm (because there are two optimal arms). You just want to run the experiment until you?re sure that switching arms won?t help you very much.
Overview of Content Experiments - google.com - Your Highlight Location 72-75 | Added on Friday, 19 April 13 09:49:00
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bandits make experiments more efficient, so you can try more of them. You can also allocate a larger fraction of your traffic to your experiments, because traffic will be automatically steered to better performing pages.
Overview of Content Experiments - google.com - Your Highlight Location 25-27 | Added on Friday, 19 April 13 09:15:52
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The extra data collected on the high-performing variations can help separate the "good" arms from the "best" ones more quickly.
Overview of Content Experiments - google.com - Your Highlight Location 24-25 | Added on Friday, 19 April 13 09:14:42
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Benefits Experiments based on multi-armed bandits are typically much more efficient than "classical" A-B experiments based on statistical-hypothesis testing. They?re just as statistically valid, and in many circumstances they can produce answers far more quickly. They?re more efficient because they move traffic towards winning variations gradually, instead of forcing you to wait for a "final answer" at the end of an experiment.
Overview of Content Experiments - google.com - Your Highlight Location 21-24 | Added on Friday, 19 April 13 09:14:33
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