The process of sample size calculations, including relevant definitions, is explained and clear examples for different study designs are provided for illustration. A range of software packages and websites are discussed and evaluated
Presentation that applies the topics of power and sample size to examples in epigenetic epidemiology studies. Step by step solutions using statistical softwares G*Power and STATA are given.
Presentation that covers: the significance of sample size, determination of sample size, factors that may affect sample size, and how to use sample size in a research or study.
Determining the right sample size in a reliability test is very important. If the sample size is too small, not much information can be obtained from the test in order to draw meaningful conclusions; on the other hand, if it is too large, the information
obtained through the tests will be beyond that needed, thus time and money are wasted. This tutorial explains several commonly used approaches for sample size determination.
If you plan to use inferential statistics (e.g., t-tests, ANOVA, etc.) to analyze your evaluation results, you should first conduct a power analysis to determine what size sample you will need. This page describes what power is as well as what you will need to calculate it.
An online calculator designed to give an estimated sample size that would be needed under specific conditions. This is used only for simple random samples.
Resource providing information about: computation of the sample size and the assumptions that must be made to do so. Several examples are given with different conditions in each, and a table showing minimum sample sizes for a two-sided test.
