Experimental design and data analysis

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This page lists resources discussed in the Design and Analysis seminar and includes links to relevant further reading. Please feel free to add your own suggestions and comments to the sections. The course is run approximately yearly and takes places in the Institute of Biochemistry of the University of Tübingen. See the Institute's course page for dates and contact information.

Aim of the course

Experimental design and data analysis is a new graduate seminar piloted in 2013 to address the questions of how to plan an experiment and how to best analyze the resulting data. We look at how to do a proper background check, where to find the best protocols, how to formulate a useful hypothesis, methods to keep experiments on schedule, tools of data analysis, and finally we will talk about some psychological pitfalls frequently seen in the interpretation of results.

1. Selecting a project

2. Planning your project

Background check, methods research

  • PubMed - your classical literature database; "weak in some areas of chemistry, physics, plant science, maths & stats" ~ j
  • Google Scholar - Google's scientific material database; "pro: citation count, PDFs, con: order of articles not disclosed and older articles often on top" ~ j
  • Current Protocols - life science protocols, only paid access
  • Cold Spring Harbor Protocols - protocols for subscribers, also publisher of the widespread Molecular Cloning book series
  • JoVE - Journal of visualized experiments, some teaser videos available; see also OWW article on JoVE

Discussion forums


3. Data analysis


4. Psychological pitfalls

"Authors' conclusions.. significantly favoured experimental interventions if financial competing interests were declared." from the conclusion
"90% of privately supported but only 60% of generally supported clinical studies reach the conclusion that the new therapy is better." ~ j

See also