Over the holiday weekend, I had a chance to bring a statistics aphorism to life, as I went cherry picking at a local farm. If you’re unfamiliar with the expression, cherry-picking one’s data means selecting only those case studies or data points that reinforce your point, while ignoring the rest. This expression never made a ton of sense to me until I actually went cherry picking.
Believe it or not, half of these cherries aren’t ready to eat.
Here’s why it now makes sense. Cherry trees have a wide, wide spectrum of fruit ripeness. At any given time, on any given tree that is in season, about 5% or so of the cherries will be picture-perfect, ready to pick and eat. About 20% are reasonably close to ripe, but might need a few more days to mature. 5% or so will be past ripeness and on the way to rotten. 10% will inevitably be partially eaten by pests. The remainder will be in various stages of ripening but nowhere near ready to eat.
From a statistical perspective, if you wanted a true understanding of a tree’s ripeness, you’d randomly pick cherries from it and get a wide selection of cherries at various stages of ripeness. If, however, you wanted a more practical, more useful harvest, you’d only pick the ones that were ripe or near ripe, even though your harvest would be statistically non-representative of the tree as a whole.
Cherry picking one’s data isn’t universally bad, however. It’s bad if what you’re after is statistically representative data. It’s good if you only want to look at certain pieces of data. For example, while understanding where your entire marketing database is in terms of readiness to purchase is important, cherry-picking only those prospects who are close to buying or ready to buy makes logical sense from a resource management perspective. You want your sales and marketing efforts to focus first on those opportunities that are most ripe before they cross into overripe (and likely buy from someone else).
Understanding what your end goal is – statistically valid representation or the best of the best – will help you to understand whether cherry-picking your data is a bad or good choice.
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