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# give-feedback
w
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f
That's an interesting idea. We support metric "caps" right now where you can set a max value any single user can have. That basically stops huge outliers from screwing up results, but it still doesn't let you visualize the distribution.
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We've talked about adding histograms to the metric overview pages, but not in the context of an experiment.
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t
What’s useful about the experiment context is it respects all the targeting condition
the metric caps are certainly useful but they force you to know/guess what would be an unreasonable value. Also a visualization would give you a sense of skew to answer the question of “is this driven by power users?” which is always a nagging thought for these count metrics.
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