Does anyone think a section on using normality tests before doing a t-test is needed? I see it frequently in the rehabilitation literature.
Example:
I looked up the paper and this is what they did:
Blockquote
The Kolmogorov-Smirnov-Lilliefors test was applied to evaluate the normal distribution for each
investigated group. To detect the presence of outliers, the Grubb’s test was performed. Levene’s test was used to test for variance homogeneity. For normally distributed data and variance homogeneity, Student’s t-test was applied to access gender-specific differences and a one-way analysis of variance (ANOVA) followed by post hoc Scheffé’s test to analyze differences between cohorts. The subjects were later grouped based on the variability in the sacrum orientation and lumbar lordosis during different standing phases. Due to small size of the individual sub-groups, the non-parametric Friedman test was performed to assess the differences between repeated measurements in the subgroups, followed by post hoc Nemenyi test. Additionally, a regression analyses was applied and the coefficient of determination (R2) was calculated. P-values of <0.05 were considered statistically significant. The statistical analyzes were performed with R 3.2.5 (R-Core-Team, 2016).
In defense of the authors – their hypothesis was that there would be greater variance in the low back pain group vs asymptomatic participants, so some of these methods were understandable.
Their study found large amount of variability in sacral orientation and lordotic curvature.
I thought the following stack exchange threads were appropriate:
Anyone have more scholarly references?