Experimental design and data analysis

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

• How to choose a good scientific problem. - 2009 review by Alon U in Mol. Cell

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

• Protocol Online and its subsection BioForum
• BioTechniques - techniques, tips, and questions concerning molecular and cell biology
• SEQanswers - deep sequencing discussion forum

Other

• 5 free project planning software suites - 2013 software review by Ingenium Llc., a web design company

3. Data analysis

• overview diagram to decide which statistical test to use - in German by the University of Zürich
• A clinician-educator's roadmap to choosing and interpreting statistical tests - 2006 review
• Handbook of biological statistics - online textbook by John McDonald at U Delaware
• Absence of evidence is not evidence of absence - 1995 article by Altman and Bland, part of an excellent series on statistics
• Error bars in experimental biology - 2007 article by Cummings and colleagues
• Dealing with outliers - very detailed essay on the topic

Software

• Guide to statistics software
• video tutorial: non-parametric rank sum test with Excel - see how a basic rank sum test is done using Excel
• free Matlab alternative Octave

See also

• OpenWetWare Courses
• 2014 ILIAS course page (requires login)
• 2013 ILIAS course page (requires login)