The course as arranged around three key topics: understanding randomness; doing it (if not p then what; and design and implementation.
There are three sessions (each ~ 1 hour 15 mins), which will address the three areas. These are scheduled at:
- BST (UK): 12:00–13:15; 13:45–15:00 and 15:30–16:45
- CDT (New Orleans): 6:00 to 10:45
If possible make sure you have 20 small coins (cent, penny, etc.) for coin tossing experiments. Alternatively find a link to a coin tossing simulator.
Course notes (PDF, slides 4 per page)):
- Introduction (updated after course)
- part 1: Understanding randomness
- part 2: Doing it (if not p then what
- part 3: Design and implementation
Indicative content for each part is below, but the ficus will be on depth not volume and on material that is least likely to have been encountered in standard statistics courses. Key items highlighted.
- do you need statistics anyway
- why statistics is hard
- when to use statistics
Part 1: Understanding randomness
- getting a gut feel for the behaviour of random phenomena and in particular how easy it is to see structure when there is none
- learning how probability can help us where our intuition fails
- understanding the ubiquity of the Normal distribution and why it cannot be applied without great care to power-law data from social networks and similar phenomena.
Part 2: Doing it (if not p then what?)
- traditional (p-testing) and Bayesian statistics – what they mean and what they don’t
- philosophical differences between the two
- common issues including the role of the researcher vs the experiment.
Part 3: Design and implementation
- gaining power – how to ensure studies and experiments reveal real effects including the noise–effect–number triangle
- interpreting and visualising results.