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Statistical methods

Statistical methods appropriate in research are described with examples. Topics covered include the choice of appropriate averages and measures of dispersion to summarize data sets, and the choice of tests of significance, including t-tests and a one- and a two-way ANOVA plus post-tests for normally distributed (Gaussian) data and their non-parametric equivalents. Techniques for transforming non-normally distributed data to more Gaussian distributions are discussed. Concepts of statistical power, errors and the use of these in determining the optimal size of experiments are considered. Statistical aspects of linear and non-linear regression are discussed, including tests for goodness-of-fit to the chosen model and methods for comparing fitted lines and curves

Analysis methods by study type

  • Cohort studies
  • Case-control studies
  • Invention studies

Analysis methods by data type

  • Modelling quantitative outcome variables
  • Modelling binary data
  • Modelling follow-up data

Sample size determination

  • when testing a difference between means
  • when testing a proportion
  • when testing a relative risk
  • when testing case-control studies