parametric test

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Parametric tests were used when warranted; otherwise nonparametric tests were used.
Parametric tests were preceded by tests of their assumptions (normal distribution and homogeneity of variance).
Having achieved similar results, we hereby present a detailed description of the analysis obtained from the parametric tests.
A common assumption required by the parametric tests of statistical significance used in logistic regression, is the hypothesis of independence between observations.
To investigate differences between authority types, parametric tests of significance (t-tests and two-way analysis of variance) were used when the cases were grouped.
All density data were log-transformed to meet the assumptions of parametric tests.
In order to normalize the data and make parametric tests applicable the z-scores of all data were calculated first.
To avoid applying parametric tests to data that do not meet their requirements, a standard was applied to all continuous data before inclusion.
Second, the profound individual differences expected among young infants violate the assumption of equality of population variances (homogeneity of variance) for most parametric tests.
All statistical tests used are standard parametric tests.
In the first series of analyses, fish species were treated as independent observations, using standard parametric tests on log-transformed continuous variables.
The increased research resource use associated with collecting a larger sample of cost data meant that parametric tests could be applied.
Variables that did not meet the assumptions of parametric tests were log-transformed prior to analysis.
A log transformation was performed on the total score of internalizing problems during adulthood to reduce skewness for parametric tests.
Parametric tests were used where the assumptions for normality were met.
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