Q SCI special topics SPR/16: So You Think You Know Statistics?

Q SCI is teaming up with Statistics for a special topics course in spring quarter: Q SCI 497 B/STAT 498: “So You Think You Know Statistics?” This course should be relevant to environmental students (co-instructed by people from NOAA Fisheries and US Forest Svc) and is accessible – only a course in inferential statistics recommended.

This course can be applied to the ‘additional elective’ requirement of the Q SCI minor.


STAT 498B and QSCI 497B:

So, you think you can do statistics?


Architecture 160, TTh 9-10:20


* Is 0.05 really a small probability of error?

* What’s all this controversy about p-values?

* Can you tell whether a pattern is random?

* Is a t-test appropriate for non-normal data?

* If you read a conclusion in a published paper, what are the odds that it is true?

* Will you die from eating a bacon sandwich?

* How do you measure global mean temperature?

* What happens when you use big data to predict flu outbreaks?


These are just some of the questions we will discuss in this course, which is intended to elucidate the role of statistics in science. Examples will be drawn from a variety of fields, including ecology, climate science, genetics, psychology, economics, and demography. Readings will include published scientific papers, scientific stories in the regular media, blogs, opinion pieces, and popular science book chapters, At the end of this course, you will understand a bit more about the human brain, when to apply statistics, what to ask of a published paper, and when to be particularly cynical. The material will help you apply statistics thoughtfully and read about science with insight. Out of class work will include reading, thinking, and one independent project.  The instructors, Peter Guttorp from UW Statistics, Martin Liermann from NOAA Fisheries, and Ashley Steel from the US Forest Service, have extensive experience in applying statistics to answer scientific questions.



Probable Syllabus:

  1. What is the purpose of science and why is it hard for humans?
  2. Randomness and p-values
  3. Best practices in graphing and data exploration
  4. Common things people want to do with statistics
  5. Experiments, observations, and replication
  6. Science and statistics: The big picture
  7. Big data: Opportunities and pitfalls
  8. The role of science in everyday life
  9. Statistical thinking and climate change
  10. Student project presentations
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