{"id":68938,"date":"2022-06-10T11:34:00","date_gmt":"2022-06-10T15:34:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=68938"},"modified":"2022-06-23T08:30:34","modified_gmt":"2022-06-23T12:30:34","slug":"noise-vs-bias","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/noise-vs-bias\/","title":{"rendered":"Noise vs. Bias: What’s the Difference?"},"content":{"rendered":"\n

What’s the difference between noise vs. bias? Which one is a worse issue when it comes to making decisions?<\/p>\n\n\n\n

Noise and bias are the two main errors that occur when humans make a judgment. They are often confused, but the authors of the book Noise <\/em>make a clear distinction between the two ideas. <\/p>\n\n\n\n

Here’s how to understand<\/a> the difference between noise and bias.<\/p>\n\n\n\n\n\n\n\n

What Noise Is<\/strong><\/h2>\n\n\n\n

The authors of Noise<\/em> define noise as one of the two main errors in human judgment (the other being bias). To understand noise, we must first define judgment<\/em>.<\/p>\n\n\n\n

Judgment<\/h3>\n\n\n\n

A judgment is an attempt to mentally assign a value to something in order to choose a course of action. <\/strong>The authors break judgments down into predictions<\/em> and evaluations<\/em>. <\/p>\n\n\n\n

Predictions<\/em> aim to come as close as possible to some correct value or answer. <\/strong>The authors point out that insurance underwriters make predictions when they prepare quotes, and they\u2019re aiming at a theoretical goldilocks number (just right). If the premium is too low, the company loses money. If the premium is too high, the company loses customers. <\/p>\n\n\n\n

Likewise, doctors make predictions when they diagnose patients; they are trying to find the correct cause(s) of the patients\u2019 ailments. The authors point out that you can measure the accuracy of a predictive judgment by comparing the prediction to the correct answer once it\u2019s known.<\/p>\n\n\n\n

Other judgments are <\/strong>evaluations<\/em><\/strong>; they have no correct answer, but instead require the decision-maker to balance pros and cons as best as possible. <\/strong>The authors point out that judges make evaluations when they decide how to sentence criminals, or whether to grant asylum. Similarly, teachers make evaluations when they grade essays. The authors contrast evaluative judgments with predictive judgments by pointing out that since there is no \u201ccorrect\u201d answer to an evaluative judgment, you can\u2019t measure the accuracy of an evaluation in the same way you can with a prediction. <\/p>\n\n\n\n

As a general rule, judgments are neither purely factual nor purely opinion-based. <\/strong>A doctor reading the results of your blood panel isn\u2019t making a judgment (though if she sees an anomaly, she might make a judgment about its cause). Likewise, your preference for one band over another isn\u2019t a judgment either.<\/p>\n\n\n\n

Given how important professional judgments are in so many areas of our lives, we would certainly hope and expect that these judgments are accurate, consistent, and error-free. We expect a certain amount of deviation from one judger to the next and from one case to the next, because judgments take place in situations where we expect some room for disagreement among qualified, well-informed, reasonable people. <\/p>\n\n\n\n

Noise vs. Bias<\/h2>\n\n\n\n

To improve judgments<\/strong>, the authors argue that we need to reduce error<\/strong> as much as possible by correcting for noise and bias<\/strong>. The authors use the following metaphor to explain noise and bias: Think of a judgment as a target at a shooting range, where inconsistency = noise and inaccuracy = bias.<\/p>\n\n\n\n