Well they get angry because they know you are right on that particular account. Nevertheless you are wrong everywhere else in this thread. Read the thread over again.
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Your assessment that small effect size predictors are useful for individual level prediction or creating population level outcomes.
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Right, because the policy at highly scientifically productive countries is to hire scientists (or student scientists), not hire an ethnic group due to their ethnic group’s proclivity for science, regardless of their actual capability.
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Replying to @elliot_leonie @negatingspirit and
If you considered both kinds of info, you'd do better.
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Replying to @gcochran99 @negatingspirit and
That would be a nontrivial result actually. It’s not immediately true. You should read more on predictive modeling and machine learning. Typically adding highly correlated variables don’t add any predictive performance.
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Replying to @elliot_leonie @negatingspirit and
Group membership is not a highly correlated variable. This is just an example of regression to the mean ( for populations with different means) . https://en.wikipedia.org/wiki/Regression_toward_the_mean …
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Replying to @gcochran99 @negatingspirit and
group memebership is highly correlated to IQ, so a model of Y ~ IQ + group is not gonna be that much better than Y ~ IQ alone. If people here had any domain knowledge they would know this problem is common in all types of forecasting (credit card etc) which group is banned.
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You're absolutely wrong about this - this is a basic application of Bayes' theorem. True g, measured g (IQ), group average of IQ are your three variables. Any given IQ (measured g) for a member of a higher IQ group indicates higher g than the same IQ for a lower IQ group.
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