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The underlying distribution for all variables was evaluated for normality and variables with a non-normal distribution (TP, LF power, HF power and the LF/HF ratio) were transformed with the natural logarithm in order to obtain normality prior to analysis.
Data sets were evaluated for normality using the D'Agostino-Pearson test and by examining normal plots.
Data obtained from each experiment were first evaluated for normality and variance homogeneity, followed by the analysis of variance (ANOVA).
Data obtained were first evaluated for normality and variance homogeneity, followed by the analysis of variance (ANOVA).
Pain score data was first evaluated for normality of the distribution and subsequently dichotomised into two categories based on the 50th percentile of the distribution.
For statistical analysis, data were evaluated for normality, equal variance and sphericity, and analyzed by one-way repeated-measures analysis of variance with multiple dependent measures (MANOVA).
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We also provide an R program which produces publication quality histograms displaying embedded Q-Q plots and p-values, to evaluate for normality of the read distribution and statistical significance, respectively.
The measurements obtained for the study were evaluated for normal distribution using the D'Agostino and Pearson omnibus normality test and found the data to be non-Gaussian distributed.
Before running the described analyses, each variable was evaluated for the normality of the distribution to ensure it met the criteria of parametric statistics.
Variables were evaluated for non-normality, and variables showing skewed distribution were log transformed to achieve normal distribution.
Data were evaluated for non-normality distribution using graphical and descriptive techniques.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com