Quotes About Accuracy

Scientists in diverse disciplines were quick to adopt the least squares method. Over two centuries later, it remains the standard way to evaluate errors wherever achieving accuracy is the goal.

Judgment is not a synonym for thinking, and making accurate judgments is not a synonym for having good judgment.

It is wrong to blame anyone for failing to forecast accurately in an unpredictable world. However, it seems fair to blame professionals for believing they can succeed in an impossible task. Claims

In terms of noise, psychiatry is an extreme case.

Subjective confidence in a judgment is not a reasoned evaluation of the probability that this judgment is correct.

The first lesson is that errors of prediction are inevitable because the world is unpredictable. The second is that high subjective confidence is not to be trusted as an indicator of accuracy (low confidence could be more informative).

Some judgments are biased; they are systematically off target. Other judgments are noisy, as people who are expected to agree end up at very different points around the target. Many organizations, unfortunately, are afflicted by both bias and noise.

the accurate intuitions of experts are better explained by the effects of prolonged practice than by heuristics.

If it is the only one that comes to mind, it may be subjectively undistinguishable from valid judgments that you make with expert confidence. This is why subjective confidence is not a good diagnostic of accuracy: judgments that answer the wrong question can also be made with high confidence

people who spend their time, and earn their living, studying a particular topic produce poorer predictions than dart-throwing monkeys who would have distributed their choices evenly over the options. Even in the region they knew best, experts were not significantly better than nonspecialists.

simple mechanical rules were generally superior to human judgment.

Intuitive predictions are almost completely insensitive to the actual predictive quality of the evidence.

In other words, people who spend their time, and earn their living, studying a particular topic produce poorer predictions than dart-throwing monkeys who would have distributed their choices evenly over the options. Even in the region they knew best, experts were not significantly better than nonspecialists.

But those with the most knowledge are often less reliable. The reason is that the person who acquires more knowledge develops an enhanced illusion of her skill and becomes unrealistically overconfident. "We reach the point of diminishing marginal predictive returns

Unless you are a professional, however, you may not react very differently to a sample of 150 and to a sample of 3,000. That is the meaning of the statement that "people are not adequately sensitive to sample size.

an accurate diagnosis may suggest an intervention to limit the damage that bad judgments and choices often cause.

whenever accuracy is the goal, bias and noise play the same role in the calculation of overall error.

Statistical algorithms greatly outdo humans in noisy environments for two reasons: they are more likely than human judges to detect weakly valid cues and much more likely to maintain a modest level of accuracy by using such cues consistently.

Although Humans are not irrational, they often need help to make more accurate judgments and better decisions, and in some cases policies and institutions can provide that help.

Further experiments showed that people were driven to overstate the accuracy not only of their original predictions but also of those made by others.

People believe they capture complexity and add subtlety when they make judgments. But the complexity and the subtlety are mostly wasted—usually they do not add to the accuracy of simple models.

System noise is inconsistency, and inconsistency damages the credibility of the system.

Jumping to conclusions is efficient if the conclusions are likely to be correct and the costs of an occasional mistake acceptable, and if the jump saves much time and effort.

Experienced radiologists who evaluate chest X-rays as "normal" or "abnormal" contradict themselves 20% of the time when they see the same picture on separate occasions