Syllabus for Statistical Methods in Natural Sciences

Statistiska metoder i naturvetenskapen

A revised version of the syllabus is available.


  • 5 credits
  • Course code: 1BG391
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Biology A1N, Chemistry A1N, Earth Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G)
  • Established: 2011-03-10
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2019
  • Entry requirements:

    150 credits including 75 credits in biology and 30 credits in chemistry. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Biology Education Centre

Learning outcomes

On completion of the course, the student should be able to

  • describe statistical models
  • choose methods to evaluate different types of empirical data
  • use the most important and most common statistical methods
  • present the philosophy and the arguments behind experimental design and hypothesis testing.


The course starts from the students' knowledge about basic statistical concepts such as measures of central tendency and variation and hypothesis testing. The aim is to give a good overview over the statistical toolbox that is used for the analysis of empirical data, especially within biology. The course covers analysis of experimental data (ANOVA, ANCOVA, including block experiments, repeated measurement, nested and factorial experiments) but also observational data (regression including methods to choose predictors and evaluate models generalised linear models (GLIM) with logistic and Poisson distribution). Introduction to power analysis, multivariate analysis, resampling and permutation techniques. A short introduction to the program R is also offered.


Lectures, literature discussions and individual computer exercises (analysis of example data).


A passing grade requires both attendance at all parts and passed presentations of computer exercises.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2019

Some titles may be available electronically through the University library.

  • Quinn, Gerald Peter; Keough, Michael J. Experimental design and data analysis for biologists

    Cambridge: Cambridge University Press, 2002

    Find in the library