computing MDE for midexperiment power #4271
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lukesonnet
reviewed
Jul 11, 2025
| @@ -193,7 +200,7 @@ def power(self, scaling_factor) -> float: | |||
| num_2 = ( | |||
| adjusted_variance * self.prior_effect.mean / self.prior_effect.variance | |||
| ) | |||
| num_3 = self.target_mde | |||
| num_3 = copy.deepcopy(mde) | |||
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Why is this copy necessary? seems like num_3 is just used as an input below?
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I was just being hyper careful, I don't think a copy is needed.
lukesonnet
reviewed
Jul 11, 2025
| else: | ||
| raise ValueError("mde_bayes is None") | ||
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| # Current MDE at target power of 0.8 should result in computed power of 0.8 |
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@lukebrawleysmith , does this test look right?
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yes, and it's a good sanity check
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Currently GrowthBook customers do not have a mechanism for easily determining whether their target MDE is too large. This PR calculates the MDE during the middle of the experiment. By comparing the outputted MDE to the target MDE, the customer can see if their target MDE is much too big.
TODO:
align on how and when to surface the target MDE
all front end work