Resource Library

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.

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

Chapter from a textbook that covers the topic of sample size by giving a thorough background and then covering issues that are involved when determining the sample size.

Resource that gives a clear description of what the p-values and significance levels mean, and what statistical significance means. Graphs are used to illustrate the topics covered in this source.

Resource that covers specific topics within significance testing, including significance levels. P-values and how to determine what qualifies as being statistically significant covered. Examples are given throughout the text to further explain the concepts.

Resource that covers what significance testing is and what it is used for. Describes the significance level and how it relates to the p-value with respect to hypothesis testing. A glossary of key concepts and terms is included at the end of the document.

Short description of what the significance level is and what it takes for a result to be statistically significant.

Text resource that covers what type I and II errors are, how to Calculate an Alpha Level for one- and two-tailed tests, and why an Alpha Level of .05 commonly used. There is also a video included at the beginning of the video to explain the topics.

Covers topics of type I and II errors and significance levels. Common mistakes for these topics are given and the reasons they are incorrect are explained.

Notes on hypothesis testing and how to interpret the p-value with respect to the significance level of a hypothesis test.