| Title | Time | Room | Teacher |
|---|---|---|---|
| Experimental Design and Statistical Thinking in Modern Biological Research | 18.05.2026 09:00 - 12:00 (Mon) | TUM School of Life Sciences, großer Dekanatssaal 2. Stock, Alte Akademie 8, 85354 Freising | Dr. Sudip Das |
| Experimental Design and Statistical Thinking in Modern Biological Research | 19.05.2026 09:00 - 12:00 (Tue) | TUM School of Life Sciences, großer Dekanatssaal 2. Stock, Alte Akademie 8, 85354 Freising | Dr. Sudip Das |
| Experimental Design and Statistical Thinking in Modern Biological Research | 29.05.2026 09:00 - 11:00 (Fri) | TUM School of Life Sciences, großer Dekanatssaal 2. Stock, Alte Akademie 8, 85354 Freising | Dr. Sudip Das |
In der Veranstaltung informieren wir Sie und beantworten Ihre Fragen zu dem Prozess des Einreichens der Dissertation und weitere damit zusammenhängende Themen.
This course introduces doctoral researchers to the principles of experimental design and statistical thinking in biological research. It is specifically tailored to researchers from microbiology, cell biology, and immunology who have limited formal training in statistics.
The course focuses on designing robust experiments, understanding the assumptions behind commonly used statistical methods, and critically evaluating data analysis strategies. Special attention is given to omics-type data, multiple testing challenges, and effect size interpretation.
Rather than emphasizing mathematical detail, the course develops intuitive understanding and practical decision-making skills. Participants will work through real biological examples and discuss common pitfalls encountered in research.
- Design statistically sound experiments including appropriate controls, replication, and randomization
- Identify and mitigate batch effects and confounding factors
- Understand assumptions behind common statistical tests used in biological research
- Interpret p-values, confidence intervals, and effect sizes critically
- Understand principles of multiple testing correction
- Evaluate statistical analyses in publications and peer work
- Communicate quantitative results clearly and responsibly
- Learn the role of AI in these approaches.
The course combines fundamental theoretical sessions with interactive discussion, case studies and hands-on conceptual exercises.
Participants will have three main sessions:
1. Learn to design statistically sound and well controlled experiments in all branches of biology with the help of case studies and hypotheticals.
2. Learn to statistically analyse biological data with example datasets and/or “bring your own data”. The format emphasizes active participation and reflection.
3. Journal club sessions where they will critically assess published work and discuss changes.
☒ Beginn der Promotion / Beginning of the doctorate
☒ Während der Promotion / During the doctorate
☒ Endphase der Promotion / End of the doctorate
None but basic familiarity with biological research methods is helpful but not required.
Participants are encouraged to bring a laptop for exercises. No specific software is required.
Two options but not mandatory:
1. Participants are invited to bring a short description of an experiment they are currently planning or analyzing.
2. Participants are invited to bring a paper, where the figures or tests they liked or disliked or want to discuss.
The course is particularly relevant for doctoral candidates working with high-dimensional datasets and quantitative biological experiments or planning complex experimental designs involving multiple factors.
Dr. Sudip Das is a microbiome and immunology researcher at the Technical University of Munich, where he leads translational projects within ERC-linked and clinical research frameworks. His work integrates experimental microbiology, organoid systems, genome-resolved microbiology, and multi-omics approaches to investigate host–microbe interactions in health and disease. Trained as an infection biologist with microbiology and immunology background, he shifted focus to microbiome science later in his career building an interdisciplinary portfolio. The transition was supplemented with strong foundations in biostatistics as part of university studies in biotechnology. He has extensive experience in microbiology, immunology, comparative genomics, data integration of clinical cohorts, and the development of advanced in vitro models. Across his career (MSCA Fellow, PI on funded projects), he has supervised researchers and interdisciplinary teams, with a strong focus on rigorous experimental design, reproducible analysis, and critical evaluation of high-dimensional biological data.