Can you please explain or provide resources that explain this issue (esp. to a lay audience):
How to understand the significance of a co-relation number that studies (esp. meta-analyses) produce. The difficulty is from moving from the number to the practical implication of the number (what does it mean for the reader?)
The contrast is between an implications for a lay individual vs group/academic research purposes.
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Please note that while comments about the following example are appreciated, the main purpose of the post is to be able to deal with correlations in general not the specifics of the example
Example:, the correlation between general mental ability (GMA) and job performance is .5.
See the difficulty of interpreting that number here
https://www.facebook.com/groups/473795076132698/permalink/1072863099559223/
When a lay person sees the number .5, how can they better understand it? Is that high, low, moderate etc?? For me as an individual, is it worth considerable time and effort to raise my GMA? If I'm an employer, do I eliminate a candidate because of a low GMA ? Do I specify from the beginning that my applicant pool will be from a certain GMA etc..? How much important is GMA (.5) in comparison to a correlation of .4 or .2? If a correlation is .1, should I ignore it?
See the
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Answers/resources up to now:
1- Against Individual IQ worriers (Scott Alexander)
https://slatestarcodex.com/2017/09/27/against-individual-iq-worries/
2- A video explaining the concept "Co-relation does not equal causation"
https://www.youtube.com/watch?v=gxSUqr3ouYA
I explain more about what I mean by "resources" and "demonstrations" here
https://www.researchgate.net/post/List_of_Concept-Demonstrating_Resources
3- Highlighted Parts on Popularization of Academic Material
https://goo.gl/BhV5A2
I agree with Christopher Steinman that a full, technical meaning of correlation would be hard to capture, but I don't think that is what is required here. I personally think of covariance as the root of correlation, so that as one variable increases, it is associated with a meaningful increase or decrease in the other variable. As for a "meaningful" increase or decrease, I would say that statisticians have gone to considerable effort to determine how much association has to exist before it can be considered to be more than just a random.
Christopher Steinman
"Who could stop you?" my point is not about will I be able to get away with. My point is : is it a wise move for my benefit? Having bad teeth is irrelevant to job performance. Are you saying hiring people based on their GMA is irrelevant as well?" that doesn't make the largest effect the most important effect. A non-correlation might be even more important, which is the crux of applied rather than traditional theoretical analysis. " that is the most important bit for me... how can the lay person benefit from the research results by applying it to their personal life then? Is sharing the IRT or correlation results of results then be misleading instead of helpful for the layperson?
Also, what's the point of determining the largest effect then?
just a note that I added this to the answers section
Highlighted Parts on Popularization of Academic Material
https://goo.gl/BhV5A2
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