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Related Concept Videos

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...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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Estimation of sample size and testing power (part 5).

Liang-ping Hu1, Xiao-lei Bao, Xue Guan

  • 1Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China. lphu812@sina.com

Zhong Xi Yi Jie He Xue Bao = Journal of Chinese Integrative Medicine
|February 9, 2012
PubMed
Summary

Accurate sample size and testing power estimation is crucial for research design. This guide details methods for quantitative and qualitative data across single-group, paired, and crossover designs, aiding researchers in study planning.

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Area of Science:

  • Biostatistics
  • Research Methodology
  • Quantitative and Qualitative Research

Background:

  • Effective research design necessitates precise sample size and testing power estimation.
  • Inadequate sample size can lead to underpowered studies, risking Type II errors.
  • Standardized methods are essential for reproducible and reliable research findings.

Purpose of the Study:

  • To introduce robust methods for estimating sample size and testing power for difference tests.
  • To cover quantitative and qualitative data within single-group, paired, and crossover designs.
  • To provide practical guidance for researchers implementing the repetition principle in study design.

Main Methods:

  • Formulas for sample size and testing power estimation were derived for difference tests.
  • Methods were applied to quantitative and qualitative data across three common research designs.
  • SAS software's POWER procedure was utilized for practical implementation and validation.

Main Results:

  • The study presents clear formulas for calculating sample size and testing power.
  • Examples demonstrate the application of these formulas for various data types and designs.
  • SAS implementation provides a computational tool for researchers.

Conclusions:

  • The presented methods and formulas enhance the accuracy of sample size and testing power estimation.
  • This work supports researchers in adhering to the repetition principle for robust study design.
  • Accessible methodologies empower researchers to conduct more reliable and statistically sound studies.