This course is designed to teach computer simulation (Monte Carlo) based statistical techniques, with special emphasis on practical solutions to problems faced by behavioural scientists attempting to publish analyses of small, noisy, data sets in peer reviewed journals.

Three specific topics will be covered:

• Randomization: A method for hypothesis testing that is remarkably assumption-free, robust and powerful. Understanding how randomization testing works can let you construct novel tests when you find yourself with data that doesn't seem testable using classical statistics.
• Bootstrapping: A method which allows robust confidence interval estimation. Intelligent use of the bootstrap may allow you to convince journal editors, and anonymous referees that your non-significant result is not a reflection of poor data collection, or inadequate sample sizes, but a real, interesting and important publishable result.
• Experimental statistics: Computer-intensive simulations of statistical tests can be used to explore how robust, or fragile, those tests are to violations of their assumptions, or empirically investigate other non-obvious properties of tests.

Students will perform a modest amount of computer programming, but no previous experience in programming is required. Software will be provided.

Log onto E-Learning to download datasets, lecture notes, class readings etc.

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