Date of Award

2013

Publisher

North Dakota State University

Document Type

Thesis

Degree Awarded

Master of Science (MS)

Program

Statistics

Department

Statistics

College

Science and Mathematics

Faculty Advisor

Doetkott, Curt

Description

Recently, the idea of multiple comparisons has been criticized because of its lack of power in datasets with a large number of treatments. Many family-wise error corrections are far too restrictive when large quantities of comparisons are being made. At the other extreme, a test like the least significant difference does not control the family-wise error rate, and therefore is not restrictive enough to identify true differences. A solution lies in multiple testing. The false discovery rate (FDR) uses a simple algorithm and can be applied to datasets with many treatments. The current research compares the FDR method to Dunnett's test using agronomic data from a study with 196 varieties of dry beans. Simulated data is used to assess type I error and power of the tests. In general, the FDR method provides a higher power than Dunnett's test while maintaining control of the type I error rate.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/27025

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