Resource Library

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.

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.

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

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.

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.

Video that will teach you how to interpret the P-Value and significance level for a two-tailed hypothesis test that is not rejected.

Powerpoint that covers statistical testing and choosing the level of significance. It also shows statistical significance vs. practical significance.

Slideshow that describes what statistical quality control is and how it is used.

Video that will explain the concepts of attribute and variable data, and procedure for plotting attribute and variable quality control charts & reading different chart patterns.

The Census Bureau has made many data visualizations of the data it collects. It is a good collections of maps, treemaps, an age/sex pyramid, and of course more familiar graphs, like bar graphs.