“…but if I leave that data out, I get….”

As March draws to a close, so do my posts on histograms. There are two important benefits of using histograms still to cover.

The first is in identifying extreme data. Histograms help us quickly see these points – and the degree to which they are unique. Sadly, we often cast this data off as outliers. We think because it differs from the rest, there must have been some error in recording it, or it’s just due to some random, inexplicable event. This becomes easier when the remaining population better fits our desired outcome. Consider the following set of customer satisfaction ratings.

It’s easy to disregard those few ratings with scores less than two. We say to ourselves, “Those customers must have misinterpreted the scale,” “Those can’t be right,” or “Oh well, you can’t please everyone.” But we’d be much better off exploring those points in detail. What do they have in common? Were they all from a certain type of customer? Did they all involve interaction with a particular employee? Did they all occur on a certain day of the week, or time of day? We need to learn from these important outliers. And histograms help us find them.

Additionally, histograms help us understand the variance – or “spread” – in the data. We could, of course, do this by calculating a standard deviation, but the histogram provides an immediate visual sense for this variation, as highlighted below.

This matters because the consistency of our data provides insight on the consistency of our people and the processes that produce it. Have we designed an approach that thrills our customers? Are our employees applying it uniformly? How confidently can we use this data to predict the future? Histograms provide a quick, visual, first-pass answer to these questions, helping us better understand what’s working and what’s not.

I declared March “Hug a Histogram” month. Just like holiday spirit, try extending it throughout the entire year.

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