Course Identification

Title:

Introduction to statistics for life sciences

Code:

20263132

Lecturers and Teaching Assistants

Lecturers:

Dr. Michael Brusovansky

TA's:

Dana Yacobi, Adva Kaully, Itay Talpir

Course Schedule and Location

Year:

2026

Semester:

Second Semester

When / Where:

Monday, 09:15 - 12:00, Benoziyo Biochemistry Auditorium 191

Tutorials
Sunday, 13:15 - 15:00, WSoS, Rm A
Sunday, 10:15 - 12:00, Science Teaching Lab 1
Monday, 14:15 - 16:00, WSoS, Rm B
Wednesday, 14:15 - 16:00, WSoS, Rm A
Wednesday, 10:15 - 12:00, Science Teaching Lab 1

First Lecture:

16/03/2026

End date:

22/06/2026

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Obligatory; 4.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; 4.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Obligatory; 4.00 points

Comments

Tutorials schedule TBD

Prerequisites

No

Restrictions

Participants:

150

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Assignments:

20%

Final:

80%

Evaluation Type

Examination

Scheduled date 1

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Scheduled date 2

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Estimated Weekly Independent Workload (in hours)

N/A

Syllabus

This course will introduce students to basic and mid-level statistical methodology. The course will familiarize the students with basic statistical concepts (median, mean, variance, etc.) and carry on to more complex statistical tools such as distributions, hypothesis testing, and more. The bulk of the course is intended for covering the most popular statistical tests in use: t-tests, chi-square, ANOVA, regressions, and more. We will discuss both parametric and non-parametric statistical tests. The principal aim of the course is to instill the students with a basic understanding of statistical thought, so they can apply statistical reasoning in new and unfamiliar areas of research.
Topics to be discussed (not necessarily in the below mentioned order):
1. Basic concepts: Measurement scales; Variables; Transformations; Data plotting
2. Central tendencies; Measures of variability
3. The Normal distribution; Sampling distributions
4. Hypothesis Testing; Type I and type II errors
5. One-sample t-test: Power; Confidence Interval
6. Two-sample t-tests (matched and independent samples)
7. Probability, Combinatorics, Bayes' rule
8. The binomial distribution; chi-square test
9. Correlation & Regression
10. Multiple Regression
11. ANOVA
12. Post-hoc comparisons
13. Repeated measures designs
14. Nonparametric tests

Learning Outcomes

Upon successful completion of this course students should be be able to:
1. Execute different basic and medium level prominent statistical tests.
2. Differentiate between statistical needs and their applicable tests.
3. Apply the knowledge gained in this course in new and unfamiliar areas of research.

Reading List

Main Bibliography:
Statistical methods for psychology (8th edition)/ Howell, D. C. (Wadsworth, 2012).
Other Bibliography:
Mathematical Statistics and Data Analysis (3rd edition)/ Rice, J. A. (Duxbury Advanced, 2006).
A Handbook of Numerical and Statistical Techniques, with Examples Mainly from the Life Sciences / Pollard, J. H. (Cambridge University Press, 1977).
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511569692

Website

N/A