1. Statistical Inference Harry R. Erwin, PhD School of Computing and Technology University of Sunderland

2. Resources Rowntree, D. (1981) Statistics Without Tears. Harmondsworth: Penguin. Hinton, P.R. (1995) Statistics Explained. London: Routledge. Hatch, E.M. and Farhady, H. (1982) Research Design And Statistics For Applied Linguistics. Rowley Mass.: Newbury House. Crawley, MJ (2005) Statistics: An Introduction Using R. Wiley. Gonick, L., and Woollcott Smith (1993) A Cartoon Guide to Statistics. HarperResource (for fun).

3. Module Outline Introduction Using R Data analysis (the gathering, display, and summary of data) Probability (the laws of chance) Statistical inference (the drawing of conclusions from specific data knowing probability) Experimental design and modeling (putting it all together)

4. Statistical Inference Statistical inference is the drawing of conclusions from specific data knowing probability. Basically, you are assessing the probability of a hypothesis given your data. A null hypothesis is plausible but probably not true. You show this by demonstrating that the probability of the data you collected being generated if the null hypothesis were true is very small. This is called ‘falsifying a hypothesis’.

5. How Do You Falsify a Hypothesis?