How to design powerful scientific experiments

How to design powerful scientific experiments
Do you wish to know how to plan experiments to test hypothesis?
Are you interested in publishing that research in specialized journals?
The experimental design course is designed to learn how to develop an experimental
plan supported on the factors you control in your data collection (univariate and
multivariate). The factorial experimental design is one of the most powerful tools to
collect data in a format that highly increases paper publication chances. The
hypothesis statement is one of the key criteria used by journal editors and reviewers to
assess the quality of the paper submitted for publication. It is mostly suitable for those
unexperienced in the experimental design and for those aiming to use ANOVA (or
equivalent) in their research analysis.
You will learn how to:
develop an experimental design
collect valid data through designed sampling
apply it to scientific research
understand and evaluate experimental outcomes
analyse data from factorial designs
efficiently communicate scientific results
The course is particularly suitable for research in which quantifiable data is obtained
through the setup of experimental conditions. The course includes the opportunity to
apply in class the experimental design to your own experiments.
Course Main Contents
The experiment as a research method
Experimental method and units
Experimental planning
- Sampling (data collection)
- Statistical analysis
The importance of statistics in the experimental context
Components of the statistical test
- Null hypothesis
- Statistical test
- Critical value for the null hypothesis (p-value)
Experimental design
-Sampling representativeness
-Crossed and nested designs
-Designs for environmental impact studies (BACI & Beyond-BACI)
Analysis of experimental designs:
-Relations between variables (correlation & regression)
-Hypothesis tests (chi-square, ANOVA, MANOVA)
Statistical power
Statistical errors
What information to present in the results
Analysis of students own data or of given examples
Lecturer: Ana Silva (IST Researcher)
Duration: 20 hours
Course schedule: 02 to 10 february – 17h30 – 20h30 (every day)
Location: IST (Room to confirm)
Course Fee:
IST students and Alumni = 100 €,
Students from other Schools of ULisboa 140 €
External applicants 200 €