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Related Concept Videos

Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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.
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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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

Confidence Intervals

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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.
A...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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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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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Related Experiment Video

Updated: Feb 13, 2026

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
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R package to estimate intracluster correlation coefficient with confidence interval for binary data.

Hrishikesh Chakraborty1, Akhtar Hossain2

  • 1Duke Clinical Research Institute, Duke University, Durham, NC, USA.

Computer Methods and Programs in Biomedicine
|March 8, 2018
PubMed
Summary
This summary is machine-generated.

A new R package, ICCbin, offers researchers 16 methods to estimate the Intracluster Correlation Coefficient (ICC) and its confidence intervals for binary data. This tool aids in designing cluster randomized trials with binary outcomes.

Keywords:
Confidence interval of ICCICCbinIntracluster correlation coefficientR packageRandomized clinical trials

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Area of Science:

  • Biostatistics
  • Statistical Software Development

Background:

  • The Intracluster Correlation Coefficient (ICC) is crucial for cluster randomized trials, quantifying within-cluster response correlation.
  • Existing statistical software offers limited options for estimating ICC and its confidence intervals (CI) for binary data.
  • A need exists for comprehensive tools to address the variety of ICC estimation methods.

Purpose of the Study:

  • To develop an R package, ICCbin, for estimating ICC and its CI for binary outcomes.
  • To provide a flexible and user-friendly tool for researchers designing cluster randomized trials.

Main Methods:

  • The ICCbin package implements 16 distinct methods for ICC estimation, including ANOVA, moments-based, probabilistic, correlation-based, and resampling approaches.
  • It offers 5 different methods for estimating the confidence intervals of the ICC.
  • The package includes functionality to generate cluster binary data with an exchangeable correlation structure.

Main Results:

  • ICCbin provides two primary functions: `rcbin()` for data generation and `iccbin()` for ICC and CI estimation.
  • Users can select from a wide array of ICC and CI estimation techniques.
  • The package facilitates the generation of realistic cluster binary data for simulation studies.

Conclusions:

  • The R package ICCbin offers a flexible and accessible solution for estimating ICC and its CI for binary data.
  • It supports a broad range of estimation methods, enhancing the design and analysis of cluster randomized trials.
  • ICCbin is freely available from the CRAN repository, promoting its widespread adoption in research.