by All of these examples exhibit the confusion that often accompanies the drawing of causal conclusions from observational data. The likelihood of such confusion is not diminished by increasing the amount of data, although the publicity given to ‘big data’ would have us believe so. Obviously the flawed causal connection between drowning and eating ice cream does not diminish if we increase the number of cases from a few dozen to a few million. The amateur carpenter’s complaint that ‘this board is too short, and even though I’ve cut it four more times, it is still too short,’ seems eerily appropriate.
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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All of these examples exhibit the confusion that often accompanies the drawing of causal conclusions from observational data. The likelihood of such confusion is not diminished by increasing the amount of data, although the publicity given to ‘big data’ would have us believe so. Obviously the flawed causal connection between drowning and eating ice cream does not diminish if we increase the number of cases from a few dozen to a few million. The amateur carpenter’s complaint that ‘this board is too short, and even though I’ve cut it four more times, it is still too short,’ seems eerily appropriate.