Önder H., Cebeci Z., "Use and Comparison of Permutation Tests in Linear Models ", Ondokuz Mayıs Üniversitesi Anadolu Tarım Bilimleri Dergisi, cilt.24, ss.93-97, 2009
<p> <span style="color: rgb(17, 17, 17); font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 11px; background-color: rgb(251, 251, 243);">F and t-test are generally used to test significance of hypothesis and/or model parameters. Although parametric tests are considerably effective, they can be ineffective when the assumptions needed by model are not provided, which is a usual situation for many data sets. In this case, permutation test not affected by the assumptions can be applied as a non-parametric method. In this study, permutation tests such as permutation of raw data, permutation of residuals under full model and permutation of residuals under restricted model are compared for multiple linear regression, completely randomized designs, randomized block design and Latin square design in terms of the Type I error rates, and performance of each tests are studied via animal science data. Results from this study indicate that permutation tests yields more reliable results than parametric tests in terms of Type I error rate, and permutation tests are recommended in order to reduce Type I errors.</span></p>