Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course MAM005F valid from Autumn 2022

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  • To give participants an introduction to a statistical thinking, statistical methodology and management of empirical data in a statistical program. After the course, participants are able to perform and interpret the results of simple statistical analyzes and critically examine elementary statistics in scientific studies.
  • This course has a similar content as the course “Applied statistics” at the medical faculty, LU.

    The course includes the following three blocks:
    1) Introduction to medical statistics
    a. Study design
    b. Generalizability
    c. Basic statistical concepts
    d. Descriptive statistics

    2) Parameter estimates and hypothesis testing
    a. Basic principles
    b. P-value, confidence intervals and statistical power
    c. Common statistical tests for comparing two groups

    3) Data management
    a. Create and validate datasets
    b. Documentation
    c. Basic knowledge in a statistical package (SPSS)
    d. Reproducible analyses (script-based)

    This course discusses questions that can be studied through quantitative methods. The course discuss common statistic concepts different measures of dispersions and proper graphical techniques to visualize and study characteristics of the collected data. The course will also introduce concepts like parameter estimation, and uncertainty will be discussed and described through standard errors and confidence intervals. Additionally hypothesis testing, p-values and statistical power are introduced. The course will also cover basic tests for comparing two groups e.g., t-test, Mann-Whitney, Chi-square and Fisher’s exact test.

    Focus will be on interpretation and on which conclusions can be drawn from the results based on statistical significance, evidence, effect size and generalizability.

    The course also includes a practical introduction to a statistical package (SPSS). This session focuses on principles for data management but the participants will also get the opportunity to conduct the statistical analyses covered in the course.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to account for different types of variables and describe how they can be presented numerically and graphically
    be able to explain the concepts of sampling, parameters and parameter estimation, and to estimate uncertainty (standard error) related to sample size
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to set up the null hypothesis and describe the concepts of significance level, statistical power, confidence intervals and p-value
    be able to perform comparison between groups and justify an appropriate test (parametric or non-parametric), and know how to perform the analysis with a statistical program and interpret the results
Judgement and Approach
  • For a passing grade the doctoral student must
  • be able to discuss the concepts of causality and generalizability
Types of Instruction
  • Lectures
  • Seminars
  • Exercises
Examination Formats
  • Written report
  • Written assignments
  • Active participation (>80%) and written report.
  • Failed, pass
Admission Requirements
  • Admission to postgraduate studies.
Assumed Prior Knowledge
Selection Criteria
  • Björk, J.: Praktisk statistik för medicin och hälsa.. Liber, 2010.
  • Alternative in English: Kirkwood B and Sterne J. Essential Medical Statistics. Blackwell Science, 2nd edition, 2003. Chapter B: 2-8 and C: 14, 15 and 17.
Further Information
Course code
  • MAM005F
Administrative Information
  • 2022-08-29
  • Åsa Håkansson

All Published Course Occasions for the Course Syllabus

1 course occasion.

Start Date End Date Published
2022‑04‑22 (approximate) 2024‑12‑31

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