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What many early studies found was that if two groups are identical, meaning that they have equal distributions, Students t test and more generally the ANOVA F test are robust to non-normality, in the sense that the actual probability of a type I error would be close to the nominal level.
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For normal distributions, student t-test was used.
Flexible distributions (Student- t distribution or mixture of Dirichlet processes) would make easier the modelling of a risk-score distribution in the diseased and non-diseased groups.
And according to Jim Cuomo, president of Ryko Distribution, student labels represent the future of an industry in flux.
The two hospitals showed differences in patient gender, age, chief complaint, procedure distribution, student involvement, and encounters related to shift time and location.
For variables with normal distribution student's t-test was used to compare differences between two independent groups.
Post hoc comparisons were performed with the appropriate test according to data distribution (Student-Newman-Keuls for parametric data and Dunn test for non-parametric data).
In Table 4, we present the distributions of students across fields of study.
Nevertheless, a transfer from the international profile model to the country specific distributions of students' achievement values is possible without further restrictions.
Table 2 summarizes the distribution of students first by gender and then by race within the math and reading z-score distribution within their schools and grades.
In order to control for slight differences in the age distribution of students at pretest and posttest, we age-standardized all proportions using the overall student age distribution as the standard.
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