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Related Concept Videos

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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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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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A comparison between numeric confidence ratings and verbal confidence statements.

Travis M Seale-Carlisle1, Jesse H Grabman2, David G Dobolyi3

  • 1School of Psychology, King's College, University of Aberdeen.

Journal of Experimental Psychology. Applied
|October 15, 2024
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Summary
This summary is machine-generated.

Eyewitness confidence is more accurate when expressed in detailed numbers or words. Collecting both numeric and verbal confidence statements from eyewitnesses provides the most diagnostic information about identification accuracy.

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Area of Science:

  • Cognitive Psychology
  • Forensic Psychology
  • Decision Science

Background:

  • Eyewitness confidence is crucial in legal settings, particularly for identification accuracy.
  • The diagnostic value of confidence can depend on how it is expressed (numeric vs. verbal).

Purpose of the Study:

  • To compare the diagnostic accuracy of numeric confidence ratings across different scales.
  • To compare the diagnostic accuracy of numeric confidence ratings with verbal confidence statements.
  • To determine which format of confidence expression best predicts eyewitness identification accuracy.

Main Methods:

  • Eyewitness identification tasks were conducted.
  • Numeric confidence ratings were collected using various scales (3, 6, 21, 101 points).
  • Verbal confidence statements were compared to numeric ratings using machine-learning algorithms.

Main Results:

  • Fine-grain numeric confidence ratings (e.g., 101-point scale) were more diagnostic of accuracy than coarse-grain ratings.
  • Verbal confidence statements provided diagnostic information not captured by numeric ratings.
  • Numeric and verbal confidence expressions reflect partially independent information sources.

Conclusions:

  • Detailed numeric scales enhance the diagnostic value of confidence ratings.
  • Verbal confidence statements offer unique insights into identification accuracy.
  • Collecting both verbal and numeric confidence from eyewitnesses is recommended for maximizing diagnostic information.