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The satisfaction scores were non-normally distributed and it was not possible to transform the data to a normal distribution.
Fold changes in abaecin and defensin gene expression were box-transformed, and hymenoptaecin zero-skewness log-transformed to fit the data to a normal distribution.
We chose to fit the data to a normal diffusion model rather to an obstructed diffusion model because it is more robust and allows the fit of two distinct diffusive fluorescent populations [4].
For percentages or ratio figures, t-test was applied after arcsin √ or logarithmic transformation of the original variable to convert the binomial distribution of the data to a normal distribution.
The "juice content" variable was log-transformed prior to the analyses to fit the data to a normal distribution.
The Kolmogorov-Smirnov test will be applied to ensure the adjustment of data to a normal distribution.
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We standardized the data to have marginal normal distributions by mapping the quantiles of data to a standard normal quantile.
Low P values for goodness-of-fit suggest poor fit of the data to a single normal distribution.
A square root transformation of the data was used to reduce the skewness of the data to a more normal distribution.
We log transformed the data to achieve a normal distribution with a mean of 1.64 (SD 0.50).
However, the distributional method requires the data to follow a normal distribution and so we have sought to generalise the normal distributional method by adding a parameter and using the skew-normal distribution.
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