Comment on Say no to BAYES

• Cattail@lemmy.world ⁨5⁩ ⁨weeks⁩ ago

  You can the issue with A-B comparison is that is kinda expected for one group to be higher on average simple based on the data points you selected.

  If the averages are kinda similar and variance are high in both group A and B then I’d say both groups are statistically the same even if the statistical values are different

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  • FishFace@piefed.social ⁨5⁩ ⁨weeks⁩ ago

    So you have two groups of ten experiments, mean if group A is 100, mean of group B is 105, variance is 25 (for both groups). Obviously we are not confident that these groups differ.

    Now suppose we repeat the experiment two billion times. The group A average is now 99, and the group B average is now 103. The variance is still 25. Are you still not confident that the groups are different?

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    • Cattail@lemmy.world ⁨5⁩ ⁨weeks⁩ ago

      I’d be curious what constitution a group a versus a group b and how you get 2 billion of them.

      But I’ll interpret it as sperm on a race track since you can get 2 billion runs with one nut.

      I have to say after billion trials the averages that you calculate did come from random sample, but it would be indicative average for that group since it can’t move far from that calculated average.

      I’m visualizing the 2 billion points of both groups and seeing a bell curve with a lot of overlap. I guess they would be different, but overall very similar since the variance is pretty wide.

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      • FishFace@piefed.social ⁨5⁩ ⁨weeks⁩ ago

        Right. But that’s what p-values quantify: given the number of trials and the observed variance and means, how likely is it that the two groups are drawn from the same distribution versus actually having different means?

        So variance isn’t “more important” than p-values; high variance means that (by definition) your p-value is lower (less confident) than it otherwise would be.

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