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Related Concept Videos

Binomial Probability Distribution01:15

Binomial Probability Distribution

A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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Related Experiment Video

Updated: May 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Sample Size for a Binomial Proportion with Autocorrelation.

Amalia S Magaret1, Jeffrey Stanaway

  • 1Department of Laboratory Medicine, University of Washington, Box 359928, Seattle, WA 98195.

Statistical Communications in Infectious Diseases
|March 12, 2013
PubMed
Summary
This summary is machine-generated.

This study presents a flexible sample size calculation for repeated binary measures with autocorrelation, crucial for viral shedding studies. The method accounts for individual differences, time-based sample autocorrelation, and varying sample counts per participant.

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A Real-world What-Where-When Memory Test
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A Real-world What-Where-When Memory Test

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Last Updated: May 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trial Design

Background:

  • Repeated binary measures with autocorrelation are common in viral shedding studies.
  • Autocorrelation occurs in conditions like herpes infection due to episodic reactivation.
  • Existing sample size computations may not adequately address these complexities.

Purpose of the Study:

  • To develop a flexible sample size computation for binomial outcomes with autocorrelation over time.
  • To create a computation suitable for crossover designs, considering treatment effect dependence on pretreatment frequency.
  • To provide a robust statistical tool for planning studies with serially correlated binary data.

Main Methods:

  • Developed a sample size computation accounting for participant-level outcome frequency.
  • Incorporated autocorrelation in time between samples into the calculation.
  • Addressed varying numbers of samples per participant and crossover design complexities.

Main Results:

  • A validated sample size computation method was determined.
  • The computation accounts for key factors in repeated binary measures with autocorrelation.
  • Sensitivity analyses confirmed the robustness of the proposed methods.

Conclusions:

  • The developed sample size computation offers a flexible and accurate approach for studies with autocorrelated binary outcomes.
  • This method is particularly relevant for viral shedding and similar research areas.
  • The findings support improved study design and statistical power in relevant clinical research.