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Related Concept Videos

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...
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:
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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...
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...
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
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Sample size for testing difference between two proportions for the bilateral-sample design.

Shi-Fang Qiu1, Nian-Sheng Tang, Man-Lai Tang

  • 1Department of Statistics, Yunnan University, Kunming, People's Republic of China.

Journal of Biopharmaceutical Statistics
|February 26, 2010
PubMed
Summary
This summary is machine-generated.

This study provides accurate sample size formulas for bilateral studies with binary outcomes. Rosner

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Accurate sample size calculation is crucial for the validity and efficiency of clinical studies.
  • Existing sample size formulas may not adequately account for the complexities of bilateral studies with binary outcomes.
  • Determining appropriate sample sizes ensures sufficient statistical power to detect significant differences.

Purpose of the Study:

  • To derive and evaluate approximate sample size formulas for comparing two proportions in bilateral studies.
  • To assess the accuracy of these formulas under various statistical tests and assumptions.
  • To provide practical guidance for sample size determination in relevant research areas.

Main Methods:

  • Derivation of approximate sample size formulas for bilateral studies with binary outcomes.
  • Consideration of four distinct statistical tests for hypothesis testing.
  • Conducting simulation studies to compare the accuracy of derived formulas against desired power levels.

Main Results:

  • The sample size formula based on Rosner's statistic, incorporating a dependence assumption, demonstrated high accuracy.
  • This formula consistently achieved actual power levels close to the pre-specified desired power.
  • Simulation studies validated the performance of the proposed formulas.

Conclusions:

  • The developed sample size formulas offer reliable tools for researchers conducting bilateral studies.
  • Rosner's statistic-based formula is highly recommended for its precision in achieving target statistical power.
  • The findings are demonstrated with a practical example from an otolaryngological study.