Course Identification

Introduction to statistics for life sciences

Lecturers and Teaching Assistants

Dr. Joshua Armstrong Eby
Ofer Karp, Dr. Tamar Reitich-Stolero, Lior Gorodisky

Course Schedule and Location

Second Semester
Sunday, 09:15 - 11:00, Wolfson Auditorium

Sunday, 16:15 - 18:00,
Monday, 16:15 - 18:00,
Tuesday, 13:15 - 15:00,
Tuesday, 10:15 - 12:00,
Wednesday, 13:15 - 15:00,
Wednesday, 15:15 - 17:00,

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Obligatory; Regular; 3.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Obligatory; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Obligatory; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Obligatory; Regular; 3.00 points


Will be taught via Zoom starting April 19th.
*Obligatory for 1st year MSc students
*Obligatory for PhD students who started on October 2015 onward

*As is the case for many courses @ Weizmann, the students in this course will have heterogeneous backgrounds. Some of the course material may be familiar to students who took statistical courses during their undergraduate studies. I will provide book chapters for those students that did learn some statistics in the past, and/or for students who wish to widen their scope or to better understand the formulae used in the statistical tests that we will be learning about. In class itself, I will minimize the use of mathematical formulae to the necessary minimum.





Language of Instruction


Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Passing the exam is mandatory for passing the course

Evaluation Type


Scheduled date 1


Scheduled date 2


Estimated Weekly Independent Workload (in hours)



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).