Candidates will be required to demonstrate understanding of basic statistical concepts, but will not be expected to have practical experience of statistical methods. Emphasis will be placed on methods by which data may be summarised and presented, and on the selection of statistical measures for different data types. Candidates will be expected to understand the statistical background to measurement error and statistical uncertainty.
Descriptive statistics
Categories of data. Statistical distributions (Gaussian, chi-squared, binomial) and their parameters. Non-parametric measures of location and variability. Graphical presentation of data
Deductive and inferential statistics
Simple probability theory. Confidence intervals. Linear regression. Linear correlation
The null hypothesis. Type I and type II errors. Probability of error occurrence, and the power of a test to detect a significant difference, Bland-Altman plot. Choice of simple statistical tests for different data types
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