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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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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 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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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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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Related Experiment Video

Updated: Feb 7, 2026

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors
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Confidence in uncertainty: Error cost and commitment in early speech hypotheses.

Sebastian Loth1,2, Katharina Jettka3, Manuel Giuliani4

  • 1Social Cognitive Systems, CITEC, Bielefeld University, Bielefeld, Germany.

Plos One
|August 2, 2018
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Summary

Humans use uncertain speech recognition hypotheses to plan actions, initiating responses quickly with low error costs and waiting for more evidence when costs are high. This improves human-robot interaction timing and social appropriateness.

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Robotics

Background:

  • Artificial agents often exhibit delayed responses due to speech recognition latency.
  • Incremental speech recognition offers early, uncertain hypotheses to mitigate this delay.
  • Uncertainty in hypotheses can lead to errors and invoke error costs in human-robot interaction.

Purpose of the Study:

  • To investigate how humans utilize uncertain hypotheses from incremental speech recognition for planning and response initiation.
  • To understand the role of error cost and time pressure in human decision-making under uncertainty.

Main Methods:

  • A Ghost-in-the-Machine study was conducted in a simulated bar environment.
  • Human participants controlled a bartending robot, relying solely on its recognizer outputs.
  • Participants' decision-making processes regarding action initiation and evidence evaluation were observed.

Main Results:

  • Participants strategically used uncertain hypotheses for action selection, similar to utility computation in dialogue moves.
  • Action initiation was dependent on perceived error cost; low cost led to initiation with suggestive evidence.
  • High error costs prompted users to wait for more confident hypotheses, while time pressure with limited evidence led to using echo questions for grounding.

Conclusions:

  • Humans employ a psychologically plausible policy to manage uncertainty in human-robot interaction.
  • This policy enables more timely and socially appropriate responses from artificial agents.
  • Findings inform the development of adaptive interaction strategies for agents operating under uncertainty.