Course content

Descriptive statistics, probability, probability distributions, estimation, parametric hypothesis testing, nonparametric hypothesis testing

Learning outcome

After completing the course, the student:

• Can demonstrate factual knowledge of basic definitions used in statistics including definitions of measures of central tendency, standard deviation, binomially distributed variable, Poisson distributed variable, normally distributed variable, sampling distribution of the sample mean, sampling distribution of the proportion, point estimate, confidence interval estimate, null hypothesis, alternative hypothesis, test statistic, critical valueand p-value
• Is able to describe the results and theorems derived from covered concepts including the central limit theorem, expressions for confidence intervals, and distributions for test statistics
• Can identify and apply formulas given by definitions including calculation of measures of central tendency and standard deviations
• Is able to apply results and theorems to solve problems including computation of probabilities, computation of point estimates, computation of confidence intervals for means. proportions and rates, and performing hypothesis tests
• Has acquired a level of proficiency in the fundamental concepts and applications sufficient for further study in academic fields requiring statistics as a prerequisite, or for work in fields requiring a background in statistics

Learning methods and activities

Lectures and exercises. Approved exercises from the same or previous year are required to sit for the exam.

Compulsory assignments

• Approved exercises

Recommended previous knowledge

High School Degree in Mathematics.

Required previous knowledge

Master's degree or similar. The course is also open for medical and master's students at the Faculty of Medicine and Health Sciences. Candidates with a lower degree will be assessed individually.

Course materials

Rosner, B: "Fundamentals of Biostatistics", 8th ed. 2015