Parametric Tests in MerQur: The Full Family from One-Sample t-Test to MANOVA
DOI:
https://doi.org/10.53463/merqur.20260445Keywords:
parametric tests, t-test, ANOVA, MANOVA, bootstrapAbstract
Parametric tests are statistical methods that test hypotheses concerning population parameters under the assumption that observations are drawn from a specific parametric distribution family, most commonly the normal distribution. These tests remain among the most frequently employed analytical tools in academic research and, when properly applied, yield the highest statistical power.
This study provides a detailed introduction to eleven analytical procedures available under the Parametric Tests category of the MerQur desktop software. These include the one-sample t-test, independent two-sample t-test, paired t-test, one-way ANOVA, two-way ANOVA, repeated measures ANOVA, MANOVA, ANCOVA, bootstrap confidence intervals, permutation tests, and multiple comparison correction methods. For each analysis, the following aspects are systematically addressed: (i) the hypothesis being tested and the corresponding application context, (ii) the required assumptions (normality, homogeneity of variance, independence of observations, and sphericity), (iii) the input fields and parameter options available in the MerQur interface, (iv) the core statistics and effect size measures reported, and (v) interpretive guidance illustrated through a typical research question.
Bootstrap confidence intervals and permutation tests are included within the same category, as they provide resampling-based alternatives when the assumptions of classical parametric tests are not met. The multiple comparison correction section covers the Bonferroni, Holm, Hochberg, Benjamini-Hochberg (False Discovery Rate control), and Šidák methods, along with a discussion of their appropriate usage conditions.
In this framework, the Parametric Tests module of MerQur offers an integrated graphical interface that accommodates a broad spectrum of analytical designs, ranging from simple group comparisons to multivariate and covariance-adjusted models, while simultaneously incorporating assumption diagnostics and effect size computations.
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