Course Identification

Introduction to statistics for life sciences
20233241

Lecturers and Teaching Assistants

Dr. Tamar Reitich-Stolero
Yuval Waserman, Lior Gorodisky, Dana Yacobi

Course Schedule and Location

2023
First Semester
Sunday, 09:15 - 11:00, Wolfson Auditorium

Tutorials
Monday, 15:15 - 17:00, WSoS, Rm B
Monday, 13:15 - 15:00, WSoS, Rm B
Tuesday, 12:15 - 14:00, WSoS, Rm B
Tuesday, 15:15 - 17:00, WSoS, Rm B
Wednesday, 15:15 - 17:00, WSoS, Rm C
Thursday, 10:15 - 12:00, WSoS, Rm C
06/11/2022
10/02/2023

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

Comments

*Obligatory for MSc students
*Obligatory for PhD students who started on October 2015 onward



Prerequisites

No

Restrictions

120

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

20%
80%
Passing the exam is mandatory for passing the course

Evaluation Type

Examination

Scheduled date 1

01/03/2023
Ebner Auditorium
1000-1300
N/A

Scheduled date 2

21/03/2023
Ebner Auditorium
1000-1300
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