Resource Library

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.

Article that explains why comparing statistical significance, sample size and expected effects are important before constructing and experiment.

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.

A resource providing information about what the sample size is, what factors the sample size depends on, and how it can be determined,

Powerpoint explaining what power is and how power and sample size are related to one another.

Document (pdf) illustrating a test of normality using an Anderson-Darling test in MINITAB and a test of equality of variances with an F-test in EXCEL.

This is a complete lesson module (including example problems with answers to selected problems) for the purpose of enabling students to: 1) Provide examples demonstrating how the margin of error, effect size, and variability of the outcome affect sample size computations. 2) Compute the sample size required to estimate population parameters with precision. 3) Interpret statistical power in tests of hypothesis. 4) Compute the sample size required to ensure high power when hypothesis testing.