basic assumption of parametric tests
normality
represents the probability under the assumption of the null hypothesis
p-value
effect size index for t-test
d
effect size index for ANOVA
eta squared
compares the variance of two groups
Levene’s test
effect size index for Friedman test
Kendall coefficient of concordance
corrects for multiple comparisons
Bonferroni
used when two independent groups are compared
t-test
tests used to determine normality of data
Kolmogorov-Smirnov & Shapiro-Wilk
tests for sphericity, or homogeneity of variance for repeated measures
Mauchley’s test
determined by degrees of freedom, level of significance, and tail of the test
Critical value of t
calculates variance in ANOVA
sum of squares (SS)
used when two dependent groups are compared
Paired t-test
used with one independent variable with three or more levels
one-way ANOVA
used with factorial design when two independent variables are used
two-way ANOVA
used when all subjects are tested under all levels of the independent variable
repeated measures ANOVA
used to test the difference between distribution-free independent samples
Mann-Whitney U Test
used with more than two distribution-free independent groups
Kruskal-Wallis
non-parametric test used to compare two related groups
Wilcoxon signed-rants test
non-parametric test used to compare three or more repeated conditions
Friedman test