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Apply suggestions from code review

Co-authored-by: sighphyre <liquidwicked64@gmail.com>
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Thomas Heartman 2021-12-10 13:53:49 +01:00 committed by GitHub
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@ -33,7 +33,7 @@ The simplest A/B experiments use a control group and a single treatment group, b
### Potential pitfalls ### Potential pitfalls
A thing to keep in mind when running experiments like this or in other cases where you're optimizing for a single metric is whether this is damaging to certain other metrics. Does more sign-ups also lead to more people (relatively) cancelling their membership? Does it decrease engagement with other parts of your product? A thing to keep in mind when running experiments like this or in other cases where you're optimizing for a single metric is whether this is damaging to certain other metrics. Do more sign-ups also lead to more people (relatively) cancelling their membership? Does it decrease engagement with other parts of your product?
Don't do yourself a disservice by chasing one metric above all else. Keep an eye on other metrics at the same time and see if they are affected — always maintain a holistic view of things. Don't do yourself a disservice by chasing one metric above all else. Keep an eye on other metrics at the same time and see if they are affected — always maintain a holistic view of things.