{"id":2911,"date":"2015-10-29T14:18:00","date_gmt":"2015-10-29T18:18:00","guid":{"rendered":"http:\/\/www.artsjournal.com\/artfulmanager\/?p=2911"},"modified":"2015-10-29T14:32:09","modified_gmt":"2015-10-29T18:32:09","slug":"chance-cost","status":"publish","type":"post","link":"https:\/\/www.artsjournal.com\/artfulmanager\/main\/chance-cost.php","title":{"rendered":"The chance and cost of being wrong"},"content":{"rendered":"

I’ve been reading a lot lately about data-informed decision making…more than is likely healthy for me. And so much of what I read begins and ends with the assumption that more data is always better. The texts jump right in to how<\/em> you collect, analyze, interpret, and report data, as if it’s always a net benefit to do so. Few actually pause to explore when<\/em> and why<\/em> you should measure at all.<\/p>\n

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\"Abacus\"<\/a>

cc flickr Sami<\/p><\/div>\n

Which is why I admire and appreciate Douglas W. Hubbard’s book, How to Measure Anything<\/em><\/a>. Hubbard certainly explains the how<\/em> of many approaches to data gathering and analysis (in rather relentless detail). But he also spends a good portion of the book exploring the when<\/em> and why<\/em>. From his perspective, more information can certainly have value to a decision, but it also has a cost. And unless that value exceeds that cost, you should be spending your time, money, and attention somewhere else. <\/p>\n

The two factors that determine the value of additional information, according to Hubbard, are: <\/p>\n