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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Accuracy and Precision01:52

Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Does thinking about God increase acceptance of artificial intelligence in decision-making?

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Related Experiment Video

Updated: Sep 24, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Overprecision is a property of thinking systems.

Don A Moore1

  • 1Haas School of Business, University of California at Berkeley.

Psychological Review
|May 5, 2022
PubMed
Summary
This summary is machine-generated.

Overprecision, or excessive judgment certainty, stems from ignoring potential errors. This cognitive bias affects both humans and artificial intelligence, highlighting the importance of considering all possible mistakes.

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Area of Science:

  • Cognitive Psychology
  • Artificial Intelligence Ethics
  • Decision Science

Background:

  • Overprecision is defined as excessive certainty in judgment accuracy.
  • Existing theories do not fully explain the phenomenon's prevalence.

Purpose of the Study:

  • To propose a novel theory explaining the cognitive mechanisms behind overprecision.
  • To account for the influence of question format on reported overprecision.
  • To explain the occurrence of overprecision in both humans and AI agents.

Main Methods:

  • Theoretical analysis of judgment formation.
  • Examination of cognitive biases in decision-making.
  • Comparative analysis of human and artificial intelligence (AI) judgment.

Main Results:

  • Overprecision arises from neglecting potential errors in judgment.
  • Difficulty in considering all possible ways to be wrong contributes to overprecision.
  • The proposed theory explains the impact of question framing on overprecision levels.

Conclusions:

  • The neglect of potential errors is a key driver of overprecision.
  • This theory provides a unified explanation for overprecision across human and artificial intelligence.
  • Understanding overprecision is crucial for improving judgment accuracy in various domains.