Dynomight has made the case that controlling for variables does not usually work. I. Controlling for variables in statistics means calculating the relationship between two variables that would be expected when holding another set of variables constant. For example, in Kirkegaard and I’s study on honesty and intelligence, we found that honest people tended to be more intelligent. Theoretically this could be confounded by race or the fact that people born into high status homes are more intelligent and honest. To deal with this problem, we used
What would you say to someone who points out Jews are often associated, at least by stereotypes, to be both intelligent and dishonest?
It comes up regarding some subsets of Asian groups but not as popularly and with as much of a following as Jews in the West. Is this something you looked into in a manner consistent with the topic of this article?
'An interaction effect that is over p = .001? Ignore. (Maybe above .00002???)'
What's the problem here?
Also, is it true in your opinion that all other statistically significant effects should be regarded as uninterpretable if interactions among fixed effects are found?
I cited the serotonin literature review from SSC - the 0.00002 joke comes from a .00002 finding that didn't replicate.
>Also, is it true in your opinion that all other statistically significant effects should be regarded as uninterpretable if interactions among fixed effects are found?
'I cited the serotonin literature review from SSC - the 0.00002 joke comes from a .00002 finding that didn't replicate.'
Aha I see; stats jokes are a bit over my head but I gather it's ALSO funny (sort of) because the relationship between serotonin levels and depression is highly contested (right?!)
What would you say to someone who points out Jews are often associated, at least by stereotypes, to be both intelligent and dishonest?
It comes up regarding some subsets of Asian groups but not as popularly and with as much of a following as Jews in the West. Is this something you looked into in a manner consistent with the topic of this article?
'An interaction effect that is over p = .001? Ignore. (Maybe above .00002???)'
What's the problem here?
Also, is it true in your opinion that all other statistically significant effects should be regarded as uninterpretable if interactions among fixed effects are found?
I cited the serotonin literature review from SSC - the 0.00002 joke comes from a .00002 finding that didn't replicate.
>Also, is it true in your opinion that all other statistically significant effects should be regarded as uninterpretable if interactions among fixed effects are found?
No opinion.
'I cited the serotonin literature review from SSC - the 0.00002 joke comes from a .00002 finding that didn't replicate.'
Aha I see; stats jokes are a bit over my head but I gather it's ALSO funny (sort of) because the relationship between serotonin levels and depression is highly contested (right?!)
'No opinion.'
No me neither