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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...
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,...
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.
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...
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...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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Related Experiment Video

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10:26

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Published on: September 11, 2021

Estimation of sample size and testing power (Part 1).

Liang-ping Hu1, Xiao-lei Bao, Shi-guo Zhou

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

Zhong Xi Yi Jie He Xue Bao = Journal of Chinese Integrative Medicine
|October 22, 2011
PubMed
Summary
This summary is machine-generated.

This study explains sample size estimation and power analysis, focusing on parametric methods for population mean and probability. It provides formulas and guidance for manual calculation and SAS software implementation.

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Area of Science:

  • Biostatistics
  • Statistical inference
  • Research methodology

Background:

  • Accurate sample size is crucial for reliable research findings.
  • Power analysis ensures studies can detect statistically significant effects.
  • Parametric methods are commonly used for continuous and categorical data.

Purpose of the Study:

  • To introduce fundamental concepts of sample size estimation and power analysis.
  • To detail parametric approaches for estimating sample sizes for population means and probabilities.
  • To offer practical guidance on performing sample size calculations.

Main Methods:

  • Focus on parametric statistical methods for sample size determination.
  • Inclusion of formulas for estimating sample size for population mean.
  • Inclusion of formulas for estimating sample size for population probability.

Main Results:

  • Provides clear estimation formulas for key parametric scenarios.
  • Demonstrates manual calculation methods for sample size estimation.
  • Illustrates sample size estimation using SAS software.

Conclusions:

  • Effective sample size estimation is vital for research validity.
  • Parametric methods offer robust approaches for sample size planning.
  • Accessible methods, including software, facilitate accurate sample size determination.