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Because nearly all outcomes in this study were not normally distributed, they were first log-transformed to meet the normality assumption.
Normality was assessed and non-normally distributed values were log10-transformed prior to analysis to meet the normality assumptions of the test.
Regarding the other two item types (i.e., Dis and Non), because the data did not meet the normality assumption, Kruskal Wallis tests were performed, with Mann–Whitney U tests as follow-up procedures whenever applicable (the alpha level was adjusted to.017 with Bonferroni corrections).
Regarding the effects of implicature type on test performance, because the data did not meet the normality assumption, Friedman tests were performed, with Wilcoxon tests as follow-up procedures whenever applicable (the alpha level was adjusted to.017 with Bonferroni corrections).
TAS levels were Ln-transformed to meet the normality requirement.
The data were transformed into natural log to meet the normality assumption for some data.
Similar(38)
The new data sequence meets the normality requirements.
Since the distribution of the data for both groups met the normality criterion (p >.05), parametric statistics was used for data analysis.
For each environmental matrix (n = 24), HM accumulation was checked to see if it meets the normality assumptions (Shapiro-Wilcoxon test, df = 1,23).
The distribution of residuals obtained from the models met the normality assumption.
Outcome variables met the normality assumptions by P-P plots.
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