My microeconometrics professor used to show off his icecream consumption versus drownings dataset that could pass all the significance tests he would be teaching that semester. That one always stuck with me.
Is that the best example to use, though? Ideally to promote skepticism you want correlations which are the result of sifting through mountains of data for coincidences, or correlations where the only underlying causation is something grossly general like “things often change monotonically for decades as time advances”. With “ice cream consumption versus drownings”, I wouldn’t be surprised if there’s a real, specific common factor: high temperatures motiving people to eat more cold treats and go swimming more often.
My microeconometrics professor used to show off his icecream consumption versus drownings dataset that could pass all the significance tests he would be teaching that semester. That one always stuck with me.
Is that the best example to use, though? Ideally to promote skepticism you want correlations which are the result of sifting through mountains of data for coincidences, or correlations where the only underlying causation is something grossly general like “things often change monotonically for decades as time advances”. With “ice cream consumption versus drownings”, I wouldn’t be surprised if there’s a real, specific common factor: high temperatures motiving people to eat more cold treats and go swimming more often.