In statistical analysis of toxicity data, parametric and non-parametric approaches are selected based on the characteristics of the data and the objectives of the analysis.
Parametric Analysis
This approach is used when toxicity data approximate a normal distribution and exhibit homogeneity of variance across groups. It relies on parameters such as mean and variance, employing methods like ANOVA and Dunnett’s test.
Advantages
- Yields statistically powerful and precise results when assumptions are met.
- Efficient for small sample sizes (e.g., 3–5 replicates).
Disadvantages
- Inaccurate results may occur if data deviate from normality or variances are unequal.
- Non-normal data may require transformations (e.g., logarithmic).
Non-Parametric Analysis
This approach makes no assumptions about data distribution and uses ranks or medians rather than parameters like mean or variance. Examples include the Kruskal-Wallis and Mann-Whitney U tests.
Advantages
- Robust to non-normal data, outliers, or small sample sizes.
- Applicable without data transformation.
Disadvantages
- Lower statistical power compared to parametric methods for normally distributed data.
- Limited in analyzing complex interactions.
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