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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Confidence Coefficient01:24

Confidence Coefficient

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 both the...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

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An R-Based Landscape Validation of a Competing Risk Model
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dynConfiR: An R package for sequential sampling models of decision confidence.

Sebastian Hellmann1,2, Michael Zehetleitner3, Manuel Rausch3,4,5

  • 1Chair of Behavioral Research Methods, TUM School of Management, Munich, Germany. sebastian.hellmann@tum.de.

Behavior Research Methods
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces dynConfiR, an R package for modeling decision-making. It simultaneously models choice, response time, and confidence using sequential sampling models, aiding researchers in analyzing perceptual decisions.

Keywords:
Cognitive modelingConfidenceDecision-makingDrift-diffusion modelsR packageSequential sampling models

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometrics

Background:

  • Sequential sampling models are foundational for understanding decision-making.
  • Perceptual decisions involve interconnected choices, confidence judgments, and reaction times.
  • Simultaneously modeling these variables offers a more comprehensive understanding of decision processes.

Purpose of the Study:

  • To introduce dynConfiR, an R package implementing various sequential sampling models.
  • To provide tools for fitting parameters, predicting outcomes, and simulating data for decision-making research.
  • To facilitate the analysis of choice, response time, and decision confidence.

Main Methods:

  • Implementation of multiple sequential sampling models in R.
  • Development of probability density functions and high-level fitting functions.
  • Step-by-step workflow for data preprocessing, model fitting, prediction, comparison, and assessment.

Main Results:

  • The dynConfiR package offers robust computations for probability density calculations.
  • Parameter and model recovery analyses demonstrate the reliability of the implemented models.
  • The package provides intuitive usability and high flexibility for researchers.

Conclusions:

  • dynConfiR enhances the modeling of decision-making by integrating choice, response time, and confidence.
  • The package supports researchers in analyzing empirical data and advancing the understanding of decision processes.
  • It lowers the technical barrier for applying complex computational models in cognitive science research.