Experimental design and data analysis

From OpenWetWare

(Difference between revisions)
Jump to: navigation, search
m (2. Planning your project)
(3. Data analysis)
Line 32: Line 32:
== 3. Data analysis ==
== 3. Data analysis ==
* [http://www.methodenberatung.uzh.ch/datenanalyse.html overview diagram to decide which statistical test to use] - in German by the University of Zürich
* [http://www.methodenberatung.uzh.ch/datenanalyse.html overview diagram to decide which statistical test to use] - in German by the University of Zürich
 +
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1924630/ A clinician-educator's roadmap to choosing and interpreting statistical tests] - 2006 review
* [http://udel.edu/~mcdonald/statintro.html Handbook of biological statistics] - online textbook by John McDonald at U Delaware
* [http://udel.edu/~mcdonald/statintro.html Handbook of biological statistics] - online textbook by John McDonald at U Delaware
 +
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2550545/ Absence of evidence is not evidence of absence] - 1995 article by Altman and Bland, part of an [[BMJ Statistics Notes series|excellent series on statistics]]
 +
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2064100/ Error bars in experimental biology] - 2007 article by Cummings and colleagues
* [http://rfd.uoregon.edu/files/rfd/StatisticalResources/outl.txt Dealing with outliers] - very detailed essay on the topic
* [http://rfd.uoregon.edu/files/rfd/StatisticalResources/outl.txt Dealing with outliers] - very detailed essay on the topic

Revision as of 05:20, 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

Background check, methods research

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

Discussion forums

Other

3. Data analysis


4. Psychological pitfalls

See also

Personal tools