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Related Concept Videos

Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Uncertainty in Measurement: Accuracy and Precision03:37

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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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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Related Experiment Video

Updated: Nov 11, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
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Uncertainty and Exploration.

Samuel J Gershman1

  • 1Department of Psychology and Center for Brain Science, Harvard University.

Decision (Washington, D.C.)
|March 26, 2021
PubMed
Summary

Humans use both random and directed exploration strategies to solve the explore-exploit dilemma by independently controlling uncertainty computations. This research sheds light on decision-making under uncertainty.

Area of Science:

  • Decision-making
  • Reinforcement Learning
  • Cognitive Science

Background:

  • The explore-exploit dilemma involves balancing information gathering with reward maximization.
  • Algorithmic solutions often rely on managing uncertainty about action values.
  • Existing algorithms employ distinct strategies for handling uncertainty.

Purpose of the Study:

  • To investigate how humans navigate the explore-exploit dilemma.
  • To differentiate between random and directed exploration strategies in human behavior.
  • To examine the role of total versus relative uncertainty in human exploration.

Main Methods:

  • A multi-armed bandit experiment was designed.
  • Total and relative uncertainty were orthogonally manipulated.
Keywords:
Bayesian inferenceexplore-exploit dilemmareinforcement learning

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  • Human participants' exploration behaviors were analyzed.

    • Main Results:

    • Humans utilize both random and directed exploration strategies.
    • These strategies are controlled by distinct uncertainty computations.
    • The findings reveal independent control mechanisms for different exploration approaches.

    Conclusions:

    • Human exploration is a flexible process, not solely reliant on one strategy.
    • Understanding these independent computations offers insights into adaptive decision-making.
    • This research bridges computational algorithms and human cognitive processes.