An introduction to the methods of statistical inference is developed so that common statistical techniques used in medical science may be effectively evaluated. This module will enable the learner to apply hypothesis tests and interval estimation techniques in medical research. The accurate and effective reporting of results from formal statistical data analysis will be covered. Two group and multiple group comparisons will be developed in the small sample, large sample and non-parametric settings. Statistical regression models and their use in risk effects analysis across the medical research domain will also be covered. The use of up-to-date statistical software to apply statistical models to medical data will form a core component of this module (e.g. SPSS, R, SAS).
Introduction to statistical inference:
one-sample confidence intervals and hypothesis tests for population means and proportions.
Confidence interval and hypothesis tests for the difference between two groups:
large sample inference; student's-t based inference; Mann-Whitney U test. Independent and dependent data situations.
Statistics for contingency tables:
chi-squared based test; risk ratios and odds ratios.
Simple linear regression:
least squares; model fitting and interpretation; hypothesis tests and confidence intervals.
Multiple regression:
OLS including both continuous and discrete predictors; parameter interpretation. Hypothesis testing and interval estimation. Models including only categorical predictors; one-way ANOVA and comparing k groups; pairwise comparison methods.
Lectures, tutorials and Computer laboratory sessions using suitable statistical software e.g. SPSS/R/SAS.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 60 |
| Other Assessment(s) | 40 |