| dc.contributor.author | Kubat, Jamie | |
| dc.description.abstract | 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. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Comparing Dunnett's Test with the False Discovery Rate Method: A Simulation Study | en_US |
| dc.type | Thesis | en_US |
| dc.date.accessioned | 2017-12-12T20:11:15Z | |
| dc.date.available | 2017-12-12T20:11:15Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | https://hdl.handle.net/10365/27025 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Science and Mathematics | en_US |
| ndsu.department | Statistics | en_US |
| ndsu.program | Statistics | en_US |
| ndsu.advisor | Doetkott, Curt | |
| ndsu.advisor | Magel, Rhonda | |
