Imagine the most delicious meal you could ever eat. For starters, it’s Kalamata olive tapenade on garlic ciabatta or mussels roasted in lemon juice and coriander. Then it’s rack of lamb on a bed of puréed sweet potatoes or pan-fried Aberdeen Angus steak or saffron rice with marinated lobster and a fizzy cola-bottle garnish. Yes, sweets are delicious but they’re totally out of place on a dinner plate. This is not an ad for a grocery shop. This is cutting-edge statistical modelling.
The easy availability of data makes it tempting to add just one more variable to your model. You never know, it might tick your r-squared up another percent or so. When people had to toil in the fields to grow some potatoes, carrots, and onions, they made a perfectly adequate stew. With the befuddling scale of supermarketing, people were no longer satisfied with simplicity and had to add one more sprig or a pinch more gummy-bear essence. It’s part of the reason why statistical models like economic forecasts leave such a bad taste in your mouth.
Too many data spoil the broth
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