Overview
| Module code | BIO-06.61-015 / BMARSYS-18.61-847 |
| Instructors | Dr. Saskia Otto |
| Prerequisites | Data Science 1 |
| License | CC-BY-SA 4.0 International |
The second module covers the fundamentals of statistics and experimental design. Building on the R skills from Data Science 1, you learn how to collect data, evaluate sampling plans and experimental designs, and conduct statistical hypothesis tests. The focus is on classical frequentist approaches and their application in biological research using R.
Learning Objectives
After completing this module, students can:
- apply fundamentals of descriptive and inferential statistics
- evaluate sampling designs and experimental plans
- conduct and interpret statistical hypothesis tests
- build and check linear models in R
- verify and diagnose model assumptions
- appropriately report results of statistical analyses
Vorlesungsfolien (SoSe 2026)
Die interaktiven HTML-Vorlesungsfolien wurden von Saskia Otto mit Quarto revealjs erstellt. Beim Betrachten der Präsentation ermöglichen folgende Tastaturkombinationen unterschiedliche Anzeigemodi:
- o zeigt den Übersichtsmodus an
- w wechselt in den Breitbandmodus
- f wechselt in den Vollbildmodus
- h erlaubt das Hervorheben von Code
- ctrl (Windows) bzw. cmd (Mac) UND + / - zum rein- und rauszoomen
- p öffnet ein Pop-up Fenster für zusätzliche Informationen (funktioniert allerdings nicht bei Safari)
- mit esc kann wieder in den normalen Modus gewechselt werden.
Lizenz der Vorlesungsfolien
Diese Arbeit ist lizenziert unter einer Creative Commons Attribution-ShareAlike 4.0 International License mit Ausnahme der entliehenen und mit Quellenangabe versehenen Abbildungen.
Accompanying Learning Materials
- Moodle course: UHH MIN Login
- RStudio Server/Posit Workbench of the Department of Biology: the URL is provided via the Moodle course (login credentials are sent by email)
- RStudio Server via JupyterHub of the MIN Faculty: https://code.min.uni-hamburg.de/hub/ (access via BAN credentials)
- swirl courses: DSBswirl – interactive exercises in R
- Cheatsheets: Reference cards on statistics with R
- Case studies: Showcases from the course
Literature Recommendations
Books
- German:
- Bärlocher, F. (1999): Biostatistik – Praktische Einführung in Konzepte und Methoden, Thieme Verlag, 206 pp.
- Eickhoff-Schachtebeck, A. & Schöbel, A. (2014): Mathematik in der Biologie, Springer Spektrum, 277 pp.
- English:
- Crawley, M.J. (2013): The R Book, 2nd edition, Wiley & Sons, West Sussex, UK, 945 pp.
- Quinn, G.P. & Keough, M.J. (2002): Experimental Design and Data Analysis for Biologists, Cambridge University Press, UK, 553 pp.
- Lazic, S.E. (2017): Experimental Design for Laboratory Biologists – Maximising Information and Improving Reproducibility, Cambridge University Press, 422 pp.
- Cohen, J. (1988): Statistical Power Analysis for the Behavioral Sciences, 2nd edition, Lawrence Erlbaum Associates, Hillsdale, NJ, 567 pp.
Articles on Experimental Design
- English:
- Underwood, A.J. (2009): Components of design in ecological field experiments, Annales Zoologici Fennici, 46(2): 93-111
- Hurbert, S.H. (1984): Pseudoreplication and the design of ecological field experiments, Ecological Monographs 54(2): 187-211
- Dutilleul, P. (1993): Spatial Heterogeneity and the Design of Ecological Field Experiments, Ecology 74(6): 1646-1658
- Krzywinski, M., Altman, N. & Blainey, P. (2014): Nested designs, Nature Methods 11: 977–978
- Altman, N. & Krzywinski, M. (2015): Split-plot design, Nature Methods 12: 165–166
- Stallings, W.M. & Gillmore, G.M. (1971): A Note on ‘Accuracy’ and ‘Precision’, Journal of Educational Measurement 8(2): 127-129
- Cohen, J. (1992): A Power Primer, Psychological Bulletin 112(1): 155-159
- Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007): G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39: 175-191.