General provisions

The content, length and level of the education are regulated by chapter 1, section 9 of the Higher Education Act 1 (1992:1434), by the Higher Education Ordinance (1993:100), and by the Ordinance on changes to the Higher Education Ordinance (2006:1053).

The course is given in semester 5 in the Biomedical Laboratory Science programme and consists of theoretical studies and problem-solving.

Course aims to provide students with basic knowledge in statistical data analysis with a focus on laboratory medicine so that they can apply this knowledge in different forms of biomedical laboratory analyses and bioinformatics. They should also be able to communicate with the statistical expert knowledge that is required for an understanding of later courses in the program and be able to decide when an expert is needed in their future profession.

Entry requirements

1) 90 passed credits in subjects equivalent to year 1 and 2 in the program, and at most 2 examinations left of which no more than 1 from year 1.

2) For admission to the course as a separate course, an older biomedical science education or equivalent skills is/are required.

Learning outcomes

SKILLS AND ABILITY

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

- basic methods for descriptive statistics

- basic probability theory

- basic statistical inference

- basic multivariate data analysis

KNOWLEDGE AND UNDERSTANDING

On completion of the course, the student should :

- Be able to describe the basic theoretical and practical concepts in descriptive statistics, probability theory, statistical inference and multivariate data analysis.

- Be able to describe and generalise based on examples of statistical data analysis within analytical laboratory science and bioinformatics derived from, inter alia, earlier and later courses in the program.

EVALUATION ABILITY AND ATTITUDES

On completion of the course, the student should :

- be able to evaluate the importance of and the need for calling statistical expert support for a given problem.

Content

STATISTICS

General concepts and descriptive statistics. Measurement practice performance measurements such as precision, accuracy, bias, limit of detection, variation coefficient. Basic concepts such as correlation measure, distribution functions, random samples and sampling distribution. Statistical inference theory including parameter estimation, regression analysis and parametric and non-parametric hypothesis testing. Performance measurements such as sensitivity, specificity, positive predictive value. Orientating introduction to multivariate data analysis in the form of e g hierarchical cluster analysis and multivariate regression.

APPLICATIONS

Laboratory science and bioinformatic applications such as instrument calibration, method development and validation, multiple sequence analysis, biomarker-generating methods such as spectroscopic technologies and molecular systems analyses and automatic microscopy.

Instruction

Teaching includes lectures, computer and calculation exercises, problem-oriented group assignments and seminars.

Assessment

To receive a pass for the course, all compulsory parts and a passed written examination are required. 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 University's disability coordinator.

Students who did not pass the examination have the right to retake the examination at 4 additional occasions (= a total of 5 examinations). If special circumstances apply, the programme committee may grant additional examinations. Each time the student participates in an examination counts as one examination. Submission of a so called blank exam is counted as one examination.