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Confidence Coefficient01:24

Confidence Coefficient

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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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Confidence Intervals01:21

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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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Interpretation of Confidence Intervals01:19

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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.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Uncertainty: Confidence Intervals00:54

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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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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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The Confidence Database.

Dobromir Rahnev1, Kobe Desender2,3, Alan L F Lee4

  • 1School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA. rahnev@psych.gatech.edu.

Nature Human Behaviour
|February 5, 2020
PubMed
Summary
This summary is machine-generated.

Researchers created a large Confidence Database with over 8,700 participants and 4 million trials to study confidence ratings. This resource aids understanding of perceptual, memory, motor, and cognitive processes.

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Area of Science:

  • Cognitive Neuroscience
  • Psychology
  • Data Science

Background:

  • Confidence ratings are crucial for understanding cognitive processes.
  • Existing research on confidence is fragmented across various studies and fields.
  • A centralized, standardized database is needed for comprehensive analysis.

Purpose of the Study:

  • To establish a large, accessible database of confidence studies.
  • To facilitate the analysis of confidence across diverse paradigms and populations.
  • To provide a resource for future research on metacognition and decision-making.

Main Methods:

  • Compiled 145 datasets from over 8,700 participants and nearly 4 million trials.
  • Structured data in a common, user-friendly format for easy analysis.
  • Included explanations for each dataset's collected data.

Main Results:

  • Demonstrated the utility of the database through four distinct analytical applications.
  • Provided precise estimations of foundational confidence-related effects.
  • The Confidence Database is publicly available and continuously updated.

Conclusions:

  • The Confidence Database is a valuable, growing resource for researchers.
  • Enables robust investigation into the mechanisms of confidence.
  • Supports advancements in understanding perception, memory, motor control, and cognition.