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"data normality" is a correct phrase and is commonly used in written English.
It refers to the state of statistical data being normally distributed, meaning that the data is evenly spread out around the average or mean value. Example: The data set for our experiment showed a high level of data normality, with the majority of values falling within one standard deviation of the mean.
Exact(60)
Data normality was checked by normal probability plots, Kolmogorov Smirnov test and coefficients of skewness and kurtosis.
In all cases, data that were not normally distributed were log transformed to achieve data normality.
Meanwhile, Shapiro-Wilk test was performed to test for data normality and values > 0.05 indicated that data were normally distributed.
When data do not follow normal distribution, log-transform was selected to improve the data normality.
Residual analysis confirmed data normality.
Paired t tests were used after determination of data normality.
Data normality was checked by using the Kolmogorov-Smirnov test.
Data normality was assessed by Shapiro-Wilk's Test of Normality.
Data normality was evaluated by one-sample Kolmogorov Smirnov test (Smirnov 1948).
Visual inspections of residual plots did not reveal obvious deviations from data normality.
Thus, we could not meet the data normality assumption of most parametric tests.
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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