autumn 2023
HEL-8047 Statistical models, conclusions and uncertainty for scientific data analysis - 7 ECTS

Type of course

The course can be taken as a singular course.

Admission requirements

PhD students and students at the Student Research Programme, or holders of a Norwegian master´s degree of five years or 3+2 years (or equivalent) may be admitted.

External PhD students and students at other student research programmes, must upload a document from their university stating that they are registered students.

Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

HEL-8024 Quantitative Research Methods 1 ects
HEL-8030 Applied Linear Regression Analysis 1 ects

Course content

The course will provide in-depth knowledge and firm skills in statistical analysis in three main blocks:

(1) Data analysis: choosing the appropriate model based on research question and data availability and quality.

(2) Statistical methods: perform correct statistical analysis, and draw valid conclusions.

(3) Communication: describing the methods chosen and the conclusions drawn for peer review publication.

The course is a PhD level for students in health sciences doing quantitative research, students in other sciences doing quantitative research on medium sized/large data sets, and students in applied natural and mathematical sciences with health-related applications, or others who want to obtain such knowledge and skills.

The course is an expansion of basic skills from master level and focuses on in-depth knowledge of data analysis and statistical methods for research purposes, where the data itself can be complex and noisy, the correct approach is not obvious, and the results are not as expected. As opposed to statistical methods taught at master level, where the data (possibly simulated) has been matched to the exercise, the research approach to statistical methods requires an in-depth understanding, which this course offers.

The main topics covered in depth are: probability, sample and distribution, assumptions, point estimators and confidence intervals, p-values, statistical significance, hypothesis testing, power, model fitting (linear regression), and interpretation of results. Pointers towards more advanced methods are given.

An introduction to R (RStudio) is given, and all exercises will have suggested solutions in R. It is therefore recommended for students to use R, although analyses can be carried out by the students' preferred tool. Students are expected to bring their own data set, and assignments and home exam are directed towards developing relevant parts of the students’ own manuscript drafts.

Objectives of the course


  • In-depth knowledge of common data analysis and statistical methods in quantitative research.
  • Assess and rank the adequateness of different methods, depending on research questions and data availability and quality.
  • In-depth knowledge of the requirements for the validity of statistical analysis results.


  • Articulate and evaluate research questions, evaluate data and data quality. Re-evaluate research question according to preliminary data analysis, and modify the hypothesis.
  • Appraise adequacy of statistical methods, and conclude regarding the validity of the results.
  • Plan and perform analysis, defend choice of method. Present and arrange analysis, results and their validity for peer-review publication. This includes so-called "null findings", i.e., not rejecting the null hypothesis.


  • Identify non-valid statistical analysis by violation of requirements, and identify alternative, correct approaches.
  • Identify basic components in a research question with matching data set, to build more complex analysis schemes.
  • Recognise own limitations in statistical analysis, and communicate needs in an adequate manner to more skilled experts for interdisciplinary research collaborations.

Language of instruction and examination


Teaching methods

The course consists of weekly lectures and seminars for 14 consecutive weeks.

The lectures will cover the course contents, the skills will be acquired through teacher-led seminars with exercises and discussions. The students are expected to hand in answers to exercises each week. Two announced exercises (assignments) are mandatory, and passing is required for taking the exam at the end of the semester. Although attendance to lectures and seminars are not mandatory, students are strongly advised to attend.

Information to incoming exchange students

PhD students and students at the Student Research Program at UiT The Arctic University of Norway register for class and exam in Studentweb by September 1st for autumn semester and February 1st for spring semester.

Other applicants apply for the right to study by June 1st for courses that are taught in the fall semester and December 1st for courses that are taught in the spring semester. Application is sent through SøknadsWeb. Please use the application code "9301 - Singular courses at the PhD level". If granted admission to the course students must register for class and exam in Studentweb by September 1st for autumn semester and February 1st for spring semester.


Examination: Date: Duration: Grade scale:
Off campus exam 01.12.2023 09:00 (Hand out)
15.12.2023 12:00 (Hand in)
2 Weeks Passed / Not Passed

Coursework requirements:

To take an examination, the student must have passed the following coursework requirements:

Mandatory assignment Approved – not approved
Mandatory assignment Approved – not approved
UiT Exams homepage

More info about the coursework requirements

Two mandatory assignments.

Re-sit examination

Re-sit examination is offered for the course.
  • About the course
  • Campus: Tromsø |
  • ECTS: 7
  • Course code: HEL-8047
  • Tidligere år og semester for dette emnet