2012
DOI: 10.5539/mas.v6n5p27
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A Robust Alternative to the t - Test

Abstract: Abstractt-test is a classical test statistics for testing the equality of two groups. However, this test is very sensitive to non-normality as well as variance heterogeneity. To overcome these problems, robust method such as F t and S 1 tests statistics can be used. This study proposed the use of a robust estimator that is trimmed mean as the central tendency measure in F t test and median as the central tendency measure in S 1 test when comparing the equality of two groups. The performance of the S 1 test wit… Show more

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Cited by 2 publications
(2 citation statements)
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“…However, the test does not perform well when normality assumption is violated and worsens when heterogeneity of variance and sample size exist [1]. Violation from these assumption will eventually lead to inflation of Type I error rate and depression in statistical power [2][3] [4][5] [6]. As alternative, nonparametric test could be the choice when the issue of violation, however, this test seems to be less powerful and the sample size need to be large enough in order to achieve reliable results [7].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…However, the test does not perform well when normality assumption is violated and worsens when heterogeneity of variance and sample size exist [1]. Violation from these assumption will eventually lead to inflation of Type I error rate and depression in statistical power [2][3] [4][5] [6]. As alternative, nonparametric test could be the choice when the issue of violation, however, this test seems to be less powerful and the sample size need to be large enough in order to achieve reliable results [7].…”
Section: Introductionmentioning
confidence: 99%
“…Violation from these assumption will eventually lead to inflation of Type I error rate and depression in statistical power [2][3] [4][5] [6]. As alternative, nonparametric test could be the choice when the issue of violation, however, this test seems to be less powerful and the sample size need to be large enough in order to achieve reliable results [7]. To alleviate the aforementioned problems, robust statistical methods are recommended.…”
Section: Introductionmentioning
confidence: 99%