photo by Elina Numminen

7.1.2020 at 08:00 - 1.3.2020 at 23:59
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Here is the course’s teaching schedule. Check the description for possible other schedules.

Mon 13.1.2020
10:15 - 12:00
Wed 15.1.2020
10:15 - 12:00
Mon 20.1.2020
10:15 - 12:00
Wed 22.1.2020
10:15 - 12:00
Mon 27.1.2020
10:15 - 12:00
Wed 29.1.2020
10:15 - 12:00
Mon 3.2.2020
10:15 - 12:00
Wed 5.2.2020
10:15 - 12:00
Mon 10.2.2020
10:15 - 12:00
Wed 12.2.2020
10:15 - 12:00
Mon 17.2.2020
10:15 - 12:00
Wed 19.2.2020
10:15 - 12:00
Mon 24.2.2020
10:15 - 12:00
Wed 26.2.2020
10:15 - 12:00
Mon 2.3.2020
10:00 - 12:00


Master's Programme in LSI is responsible for the course, and it belongs to the Eco-evolutionary informatics module.

Course is however available for students from all degree programmes.

Student should have good basic understanding on basic applied statistics and some familiarity with some empirical science (e.g. biology).

For particular courses, the following are strongly recommended:

Bayesian Inference in Biosciences (LSI35002)
Introduction to statistics

Advanced course in Bayesian inference (MAST32004).
Spatial Modelling and Bayesian Inference (MAST32005),
Ekologian perusteet (BIO-104)

After completing the course the student will develop deeper understanding on how to collect and analyze data, and how the two processes are linked.

In particular, student will assess the basic ideas behind experimental design, both controlled and observational.

Within this context, the concepts of bias, variance, correlation, confounding and causality are studied.

The course provides perspectives on how to formulate scientific questions and how to best collect data and use statistics to answer them.

-The recommended time for completion is after the Bayesian Inference in Biosciences (LSI35002) or Bayesian Inference (MAT22005) or Statistical Data Science (DATA11006).

The course is offered in period 3

  1. Introduction
  2. Controlled studies
  3. Observational studies
  4. Designing the studies
  5. Causality
  6. Advanced topics

Lecture material will be compiled by the teacher and based on several references listed below:

  • Darrell Huff: how to lie with statistics
  • Paul R. Rosenbaum: Design of Observational studies
  • R. Mead, S.G. Gilmour, A. Mead: Statistical principles for the design of experiments
  • Lindemyer & Likens: Effective ecological monitoring
  • Gerald van Belle: Statistical rules of thumb
  • several scientific articles.

Lectures and exercises.

-The course is graded based on all the submitted exercise sets.

Attending 80% of the lectures is required. In addition, exercise sets (3 in total) given during the course, and submitted to the teacher, are obligatory.