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Related Concept Videos

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...
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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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...

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Related Experiment Video

Updated: Jun 1, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Confidence interval based parameter estimation--a new SOCR applet and activity.

Nicolas Christou1, Ivo D Dinov

  • 1Department of Statistics, University of California Los Angeles, Los Angeles, California, United States of America.

Plos One
|June 10, 2011
PubMed
Summary
This summary is machine-generated.

This study demonstrates the construction and interpretation of confidence intervals using interactive web tools. These tools aid in understanding parameter estimation for various scientific and medical applications.

Related Experiment Videos

Last Updated: Jun 1, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Statistics
  • Computational Biology
  • Biostatistics

Background:

  • Accurate parameter estimation is crucial for scientific inference and prediction.
  • Variability in data necessitates robust methods for computing point and interval estimates.
  • Interactive tools can enhance the understanding and application of statistical concepts.

Purpose of the Study:

  • To demonstrate the simulation, construction, validation, and interpretation of confidence intervals using interactive web-based tools.
  • To provide examples of confidence interval calculations for various parameters and data types.
  • To showcase applications in estimating unemployment rates and analyzing neuroimaging data.

Main Methods:

  • Utilized the Statistics Online Computational Resource (SOCR) interactive web-based tools.
  • Demonstrated confidence interval construction for population means, variance, and proportions.
  • Applied bootstrapping and maximum likelihood estimation principles.
  • Presented simulation and real-world data applications.

Main Results:

  • Interactive tools facilitate empirical exploration of confidence interval behavior.
  • Confidence intervals were successfully computed for diverse parameters including unemployment rates and hippocampal surface complexity.
  • The effects of confidence level and sample size on interval estimates were explorable.

Conclusions:

  • The SOCR confidence interval resources offer accessible and interactive methods for statistical analysis.
  • These tools are valuable for education, research, and applied statistical modeling.
  • The demonstrated applications highlight the utility of robust interval estimation in scientific inquiry.