# Course Identification

Statistical principles in the analysis of research data
20203072

Dr. Ron Rotkopf
N/A

## Course Schedule and Location

2020
Second Semester
Wednesday, 13:15 - 15:00
22/04/2020

## Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 2.00 points

Will be taught via Zoom starting April 19th.
This course aims to familiarize students with executing a variety of statistical tests: t-tests, ANOVA and its different variations, multiple linear regression and more. The final assignment will contain several datasets, for which you will have to determine the appropriate statistical tests, execute the tests and report the results.

No

40

English

## Attendance and participation

Expected and Recommended

Numerical (out of 100)

100%

Take-home exam

N/A
N/A
-
N/A

3

## Syllabus

This course aims to familiarize students with executing a variety of statistical tests: t-tests, ANOVA and its different variations, multiple linear regression and more. The principle aim of the course is to enable students to understand which analysis is applicable for each type of data, and execute the proper analyses using R. The main focus will be on usability and application of statistical knowledge in answering research questions, and less on the mathematical background of the statistical methods. No background in programming is needed.

Topics by week:
1. Introduction to R
2. Descriptive statistics
3. Comparing two populations: t-test.
4. Comparing two populations - non-parametric tests
5. One-way ANOVA
6. Multiple comparisons and contrasts
7. Two-way ANOVA
8. Experimental design - randomized block, nested, split-plot
9. Experimental design - repeated measures
10. Data transformation, power calculations
11. Linear regression
12. Correlation + selected topics

## Learning Outcomes

Upon successful completion of this course students should be able to:

1. Determine the appropriate statistical analysis for a given experimental setting.
2. Plan experiments while considering statistical power, sufficient sample size, and avoiding potential statistical "pitfalls".
3. Execute a variety of common statistical tests (e.g., t-tests, ANOVA and its different variations, multiple linear regression, non-parametric analysis) using R (although the knowledge obtained will be applicable for commercial statistical software as well).
4. Critically review statistical analyses conducted in published papers.