The course, through lectures and computer lab sessions, aims to provide basics notions of statistics to plan and analyze the results of a scientific study or experiment.

At the end of the course the participants should be able to choose the most suitable design for their study, compute the optimal sample size, perform a graphical and tabular description of the data collected and analyze the association between variables through proper measures and hypothesis testing.

Course program

Day 1: 

• Planning a study: types of designs
• Data summaries: descriptive measures and graphical representa-tions.
• Lab session with R

Day 2: 

• Introduction to hypothesis testing
• Parametric tests for quantitative variables
• Lab session with R

Day 3:

• Non-parametric tests for quantitative variables
• Tests for categorical variables
• Lab session with R

Day 4:

• Correction for multiple comparisons
• Sensitivity, specificity and the ROC curve
• Lab session

Target audience: Doctoral students of any discipline who are interested in the practical application of basic statistical methods for data analysis in scientific research

Participants (min/max): 20/40