Difference between revisions of "TDSM 2.31"
From The Data Science Design Manual Wikia
(Created page with "Two events can consistently correlate with each other but not have any causal relationship. An example is the relationship between reading ability and shoe size. If someone pe...") |
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| − | Two events can consistently correlate with each other but not have any causal relationship. An example is the relationship between reading ability and shoe size. If someone performed such a survey, s/he would find that the larger shoe sizes correlate with better reading ability, but this does not mean large shoes cause good reading skills. Instead it's caused by the fact that young children have small feet and have not yet (or only recently) been taught to read. In this case, the two variables are more accurately correlated with a third: age. | + | Two events can consistently correlate with each other but not have any causal relationship. An example is the relationship between reading ability and shoe size. If someone performed such a survey, s/he would find that the larger shoe sizes correlate with better reading ability, but this does not mean large shoes cause good reading skills. Instead it's caused by the fact that young children have small feet and have not yet (or only recently) been taught to read. In this case, the two variables are more accurately correlated with a third: age. (wikipedia) |
Latest revision as of 17:41, 11 September 2017
Two events can consistently correlate with each other but not have any causal relationship. An example is the relationship between reading ability and shoe size. If someone performed such a survey, s/he would find that the larger shoe sizes correlate with better reading ability, but this does not mean large shoes cause good reading skills. Instead it's caused by the fact that young children have small feet and have not yet (or only recently) been taught to read. In this case, the two variables are more accurately correlated with a third: age. (wikipedia)
