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The number of tertiary stems and the number of buds per plant were square-root transformed to meet assumptions of normality.
For ANOVA on the time to second-degree burn the data was log10 transformed to meet assumptions of normality.
Since some of our data was dichotomous, some might argue that we did not meet assumptions, but we felt differently because we have the means of mixed data.
To address the challenges that outliers pose for some statistical models, we use the Huber–White sandwich estimators (Huber 1967; White 1980) in all our models, thereby obviating minor concerns about the potential failure to meet assumptions, such as normality, heteroskedasticity, or observations that exhibit large residuals, leverage, or influence.
Data were arcsine-transformed to meet assumptions where necessary.
LCC scores were log transformed to meet assumptions of normality and heteroscedasicity.
To meet assumptions of normality for imputation models, MVL was log-transformed.
Only green leaf number at collection required log transformation to meet assumptions of ANOVA.
Data were log-transformed when necessary to meet assumptions of normality and homogeneity of variances.
When appropriate, variables were transformed with power functions to meet assumptions of normality.
Larval masses were log-transformed to meet assumptions of normality and homogeneity of variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com