Methodology for the Evaluation of Experimental Biomedical Data

• Descriptive statistics and graphical presentation.
• Normal distribution.
• Central limit theorem.
• Sampling. Sample size calculation.
• Statistical inference confidence interval and hypothesis testing.
• Principles in design of experiments.
• Statistical inference for two populations (paired and independent).
• One-way analysis of variance (ANOVA) and multiple comparisons with fixed effects and random effects.
• Non parametric statistics: Wilcoxon signed-rank test, Mann-Whitney test and Kruskal-Wallis test.
• Two way ANOVA, interactions and multiple comparisons.
• Three way ANOVA.
• Split plot design.
• Hierarchical models. Repeated measures. Mixed models.
• Chi-square test for independence.
• Spearman and Pearson correlation.
• Simple linear regression and statistical inference. Multiple linear regression and statistical inference. Non linear regression.
• Analysis of covariance (ANCOVA).
• Survival analysis.
• Design of experiments: factorial design and optimal design.
• Calculation of size determination.
• Sampling methods (Bootstrap, Jackknife, permutations and Monte-Carlo).
• Interpretation of experimental data.