Experimental design and data analysis: Difference between revisions
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== 2. Planning your project == | == 2. Planning your project == | ||
* [http://www.ingeniumweb.com/blog/post/top-five-best-free-online-project-management-application-services/1027/ 5 free project planning software suites] - software review by Ingenium Llc., a web design company, from 2013 | |||
== 3. Data analysis == | == 3. Data analysis == | ||
Revision as of 02:51, 3 April 2014
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
- 5 free project planning software suites - software review by Ingenium Llc., a web design company, from 2013
3. Data analysis
- overview diagram to decide which statistical test to use - in German by the University of Zürich
- Handbook of biological statistics - online textbook by John McDonald at U Delaware
- Dealing with outliers - very detailed essay on the topic
- video tutorial: non-parametric rank sum test with Excel - see how a basic rank sum test is done using Excel
- free Matlab alternative Octave
