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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,...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...

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Estimation of sample size and testing power (part 6).

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
|March 14, 2012
PubMed
Summary

This study focuses on the one-factor, k-level experimental design (k ≥ 3). It provides methods for estimating sample size and statistical power for both quantitative and qualitative data with binary outcomes.

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Area of Science:

  • Statistics
  • Experimental Design

Background:

  • The one-factor, k-level design (k ≥ 3) is a fundamental experimental structure.
  • Proper sample size and power analysis are crucial for valid research findings.

Purpose of the Study:

  • To introduce methods for sample size estimation.
  • To present techniques for testing statistical power.
  • To address both quantitative and qualitative data within this design.

Main Methods:

  • The paper details statistical approaches for sample size calculation.
  • It outlines power analysis procedures for binary response variables.
  • Methods are applicable to designs with a single factor and k levels (k ≥ 3).

Main Results:

  • Provides a framework for determining adequate sample sizes.
  • Enables researchers to assess the statistical power of their studies.
  • Offers unified methods for diverse data types within the specified design.

Conclusions:

  • The proposed methods enhance the rigor of research using the one-factor, k-level design.
  • Accurate sample size and power calculations are essential for reliable conclusions.
  • This work supports robust experimental planning in various scientific fields.