Statistical Inference

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)

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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?