I have a lot of questions around when the right time to call an experiment is in the Bayesian world. We’ve been assigning users to an A/A test for a few months now, 50/50 at random. I’ve connected the experiment in Growthbook and am reviewing all my metrics. It’s great to see the small differences in effect size here, and my intuition is to use this knowledge of natural variance in future experiments. e.g. if the A/A test shows a difference of 1% for some core metric, 1% change in future experiments is probably just noise, correct? I’m just trying to assess whether the data I get from the A/A experiment is useful in that manner