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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...
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...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
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 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...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

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Related Experiment Video

Updated: May 21, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Confidence intervals for the difference of marginal probabilities in clustered matched-pair binary data.

Zhao Yang1, Xuezheng Sun, James W Hardin

  • 1Quintiles, Inc., 5927 S Miami Blvd, Morrisville, NC, 27560, USA. tonyyangsxz@gmail.com

Pharmaceutical Statistics
|June 12, 2012
PubMed
Summary

This study introduces new confidence intervals (CIs) for clustered matched-pair binary data. The best CI choice depends on the number of clusters, with recommendations for practical application in statistical analysis.

Related Experiment Videos

Last Updated: May 21, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Area of Science:

  • Biostatistics
  • Statistical Inference
  • Data Analysis

Background:

  • Limited availability of confidence intervals (CIs) for analyzing marginal probability differences in clustered matched-pair binary data.
  • Existing test statistics lack readily accessible associated CIs, hindering robust statistical inference.

Purpose of the Study:

  • To propose and investigate the performance of confidence intervals for assessing differences in marginal probabilities within clustered matched-pair binary data.
  • To evaluate the suitability of different CIs across varying numbers of clusters and intracluster correlation coefficients.

Main Methods:

  • Construction of novel confidence intervals (CIs) for clustered matched-pair binary data.
  • Monte Carlo simulation study to assess the coverage probability of proposed CIs.
  • Evaluation of intracluster correlation coefficient-adjusted McNemar statistic and alternative statistics with their associated CIs.

Main Results:

  • Proposed CIs demonstrate good performance in maintaining nominal coverage probability.
  • For small to medium clusters, intracluster correlation coefficient-adjusted McNemar statistic with Wald or Score CIs is optimal.
  • The McNemar statistic becomes conservative with larger cluster numbers, favoring alternative statistics and CIs.

Conclusions:

  • The choice of confidence interval depends on the number of clusters and data characteristics.
  • A combination of the intracluster correlation coefficient-adjusted McNemar statistic and an alternative statistic is recommended for practical applications.
  • The study illustrates practical data analysis using a real-world clustered matched-pair dataset.