Parametric and Non-Parametric Tests
Precision in Statistical Comparisons
Numerical Data
Statistical tests of numerical data are at the core of medical research, enabling researchers to compare groups, measure differences, and validate hypotheses. Parametric tests rely on specific data assumptions, including normality and equal variances, to deliver powerful and reliable results. Conversely, non-parametric tests offer robust alternatives when data deviate from these assumptions.
Right Test for Your Data
Parametric methods, including ANOVA, excel in analysing continuous data across multiple groups, while repeated measures ANOVA captures within-subject variability over time. Non-parametric methods are used to handle ordinal or non-normally distributed data, with the Mann-Whitney U test and Kruskal-Wallis test and the Friedman test for repeated measures in non-parametric contexts.
Expert Analysis
Our parametric and non-parametric testing expertise empowers your research with precision and adaptability. Whether your data aligns with traditional assumptions or requires flexible analytical approaches, we guide you towards the most suitable methods to extract meaningful insights.
Partner with us to ensure your research benefits from tailored statistical analysis that drives impactful results.