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The SAGE Encyclopedia of Research Design, 2022
A good example of a non-parametric test is the Mann-Whitney U-test (Also known as the Mann-Whitney-Wilcoxon (MWW) or Wilcoxon Rank-Sum Test). Unlike its parametric counterpart, the t-test for two samples, this test does not assume that the difference ...
Patrick E. McKnight, Julius Najab
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A good example of a non-parametric test is the Mann-Whitney U-test (Also known as the Mann-Whitney-Wilcoxon (MWW) or Wilcoxon Rank-Sum Test). Unlike its parametric counterpart, the t-test for two samples, this test does not assume that the difference ...
Patrick E. McKnight, Julius Najab
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The t-test: An Influential Inferential Tool in Chaplaincy and Other Healthcare Research
Journal of Health Care Chaplaincy, 2018The t-test developed by William S. Gosset (also known as Student’s t-test and the two-sample t-test) is commonly used to compare one sample mean on a measure with another sample mean on the same measure.
K. Jankowski, K. Flannelly, L. Flannelly
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Springer Reference Medizin, 2019
0. Summary. Consider samples from continuous distributions F(x) and F(x - 0). We may test the hypothesis 0 = 0 by using the two-sample Wilcoxon test. We show in Section 1 that its asymptotic Pitman efficiency, relative to the t-test, never falls below 0 ...
Jr . J. L. Hodges
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0. Summary. Consider samples from continuous distributions F(x) and F(x - 0). We may test the hypothesis 0 = 0 by using the two-sample Wilcoxon test. We show in Section 1 that its asymptotic Pitman efficiency, relative to the t-test, never falls below 0 ...
Jr . J. L. Hodges
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2022
This chapter explains the concept of multivariate t-tests. In comparison to univariate tests, the advantages of multivariate t-tests include greater statistical power for detecting an effect and the ability to conduct a single (omnibus) test for each variable.
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This chapter explains the concept of multivariate t-tests. In comparison to univariate tests, the advantages of multivariate t-tests include greater statistical power for detecting an effect and the ability to conduct a single (omnibus) test for each variable.
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2011
The t-test can be used to test the hypothesis that two group means are not different (Chap. 3 ). When the experimental design involves multiple groups, and, thus, multiple tests, we increase our chance of finding a difference. This is, simply, due to the play of chance rather than a real effect.
Ton J. Cleophas, Aeilko H. Zwinderman
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The t-test can be used to test the hypothesis that two group means are not different (Chap. 3 ). When the experimental design involves multiple groups, and, thus, multiple tests, we increase our chance of finding a difference. This is, simply, due to the play of chance rather than a real effect.
Ton J. Cleophas, Aeilko H. Zwinderman
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2012
We use t tests to compare means. You have already seen that the t.test function can be used to display confidence intervals for means and differences between means. The t.test function in R is used for all three kinds of t tests: one-sample, paired-samples, and two-sample t tests.
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We use t tests to compare means. You have already seen that the t.test function can be used to display confidence intervals for means and differences between means. The t.test function in R is used for all three kinds of t tests: one-sample, paired-samples, and two-sample t tests.
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2018
In studies with paired samples of continuous data the mean difference is usually compared to zero. Instead of a paired t-test also a Bayesian analysis on the mean difference is possible. A traditional paired t-test of two treatment modalities on hours of sleep provided a significant difference with t = 3.184, p-value = 0.011.
Ton J. Cleophas, Aeilko H. Zwinderman
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In studies with paired samples of continuous data the mean difference is usually compared to zero. Instead of a paired t-test also a Bayesian analysis on the mean difference is possible. A traditional paired t-test of two treatment modalities on hours of sleep provided a significant difference with t = 3.184, p-value = 0.011.
Ton J. Cleophas, Aeilko H. Zwinderman
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2018
In studies with two unpaired samples of continuous data the difference of the two means and their pooled standard error is usually compared to zero. Instead of an unpaired t-test also a Bayesian analysis on the difference of two group means is possible.
Ton J. Cleophas, Aeilko H. Zwinderman
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In studies with two unpaired samples of continuous data the difference of the two means and their pooled standard error is usually compared to zero. Instead of an unpaired t-test also a Bayesian analysis on the difference of two group means is possible.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source