Key Ideas
01
Noise refers to the undesirable variability in judgments. It can be broken down into three components: level noise, pattern noise, and occasion noise. Level noise occurs when there are differences in the average level of judgments made by different judges. Pattern noise arises from variability in judges' responses to particular cases, while occasion noise is the random variability within a judge's judgments across different occasions.
One study that illustrates the concept of system noise is the noise audit of criminal sentencing conducted by Bartolomeo et al. In this study, federal judges were asked to evaluate 16 cases and assign sentences. The results showed significant variability in the sentences assigned by different judges for the same case, indicating the presence of system noise.
Another example comes from the field of performance evaluation in organizations. Research has shown that 70-80% of the variance in job performance ratings can be attributed to system noise, with level noise and pattern noise being the main contributors. This variability can lead to inconsistencies in performance evaluations and ultimately impact employee morale and productivity.
In medicine, system noise can have serious consequences. For instance, when two doctors provide different diagnoses for the same patient, at least one of them is wrong. This variability in judgments can lead to incorrect treatments and potentially harm the patient.
Understanding and addressing the components of system noise is crucial for improving the quality of judgments in various fields. Reducing level noise can be achieved by ensuring that judges maintain uniformity in their use of cues, weights assigned to cues, and the use of the scale. Tackling pattern noise, on the other hand, requires addressing the stable individual differences that contribute to variability in judgments. By focusing on these components, organizations and professionals can work towards reducing system noise and improving the accuracy and consistency of their judgments.
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Noise is a flaw in human judgment that arises from unwanted variability in decisions. The book explores the difference between noise and bias, the psychology behind noise, and how it affects various domains such as medicine, law, and business. It also provides strategies for reducing noise and improving decision-making.
Here are 10 specific actions or main facts from the book to implement or deepen your knowledge:
Noise is different from bias. While bias refers to systematic errors in judgment, noise refers to random, unwanted variability in judgments.
Noise is often overlooked in favor of bias, but it can have significant consequences in various domains, such as medicine, law, and business.
Noise can be categorized into three types: level noise, pattern noise, and occasion noise. Level noise refers to differences in the overall severity of judgments, pattern noise refers to differences in the way people weigh various factors, and occasion noise refers to variability in judgments made by the same person at different times.
Noise can be reduced by implementing decision hygiene strategies, which are designed to minimize the influence of irrelevant factors on judgments.
One decision hygiene strategy is to break down complex judgments into simpler mediating assessments, which can help reduce the impact of noise on the final decision.
Another strategy is to use algorithms or rules to replace or supplement human judgment, as they tend to be less noisy than human decision-makers.
Eliciting and aggregating independent judgments can also help reduce noise, as it allows for a more diverse range of perspectives to be considered.
Adopting an outside view, or considering how similar situations have been handled in the past, can help reduce noise by providing a more objective perspective on the decision at hand.
Structuring meetings and discussions can help minimize the influence of noise by ensuring that all relevant factors are considered and that irrelevant factors are minimized.
Conducting noise audits can help organizations identify areas where noise is prevalent and implement strategies to reduce it, ultimately leading to more accurate and consistent decision-making.
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