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First, data were evaluated to determine whether the pre-requisites for conducting PCA were met (normality, interval-level measurement, random sampling and bivariate normal distribution) [ 42].
To remove sources of systematic variation due to experimental artifacts in the measured intensities and to ensure that the usual assumptions for statistical inferences are met (normality, homoscedasticity), we applied a variance stabilization and normalization transformation on the variables (peptides).
Change variables for continuous measures were checked and met normality assumptions.
Post-regression diagnoses were performed to ensure that all linear regression assumptions were met (normality and homoscedasticity of residuals).
Between groups comparisons were analyzed by t-tests if data met normality and equal variance tests and by Mann-Whitney rank-sum tests if they did not.
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However, a normal probability plot of each assay suggested that failure to meet normality was only attributed to one or two data points.
A parametric method is used when the data basically meets normality assumption.
All data were transformed to meet normality assumptions.
Provided that data did not meet normality assumptions, we used the Mann-Whitney test.
Variables were log10 transformed in order to meet normality and homogeneity of variances.
Values of estimated age of bedrock were log-transformed to meet normality requirements.
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CEO of Professional Science Editing for Scientists @ prosciediting.com