Statistics for biologists – A refresher course
- Start Date: November 17, 2009
- End Date: November 19, 2009
- City: Revelstoke BC
- Instructor: Dr. Carl Schwarz
- Let us know if you would like to take this course. We will hold it again if there is demand.
- You can sign up for our event announcements.
|“The earth is flat (p<0.5).”|
Many scientific studies are full of statistical jargon, tables of averages and other statistics, and results of statistical tests which purport to prove a certain hypothesis. The purpose of this course was to review some of the basic sampling and experiment designs used by ecologists and to understand exactly what can and cannot be extracted from a set of data. With the advent of modern statistical packages, the analysis of data is fairly easy, but it is far too easy to get nonsense results. This course also reviewed common pitfalls in the analysis of data.
This course is taught by Dr. Carl Schwarz from Simon Fraser University to a maximum class size of 16 people. Carl has a wealth of information about statistics for biologists posted on his website at: http://www.stat.sfu.ca/~cschwarz/. Participants are required to bring a laptop computer loaded with the current version of JMP software (trial version is adequate for the course). JMP is a SAS program.
1. Review of statistical concepts on estimates, standard errors, confidence intervals, p-values etc.
2. Overview of environmental monitoring designs
3. Overview of some basic sampling strategies
- simple random sample
- stratified sampling
- cluster sampling
- two stage sampling
- ratio estimation
4. Details on simple random sampling, stratified sampling, cluster sampling
- how to plan
- sample size requirements etc
- how to analyze
- pitfalls and which to use when
5. Overview of experimental designs (single factor, two factor)
6. Details on single factor designs
- two-sample t-test
- one way ANOVA
- pseudo-replication, pairing, blocking, etc.
7. Overview of regression analysis
8. Details on single variable regression analysis
9. Overview of categorical data analysis
10. Details on chi-square tests
11. A.I.C. Statistics (very briefly)