Postgraduate Specialisation in Sport and Exercise Medicine

Medical statistics

Course code
Name of lecturer
Lucia Cazzoletti
Number of ECTS credits allocated
Other available courses
Academic sector
Language of instruction
not yet allocated

Learning outcomes

The course aims to provide the knowledge and skills necessary to adapt inferential statistics techniques to cases when common parametric tests cannot be applied. The final goal is to analyse data on events derived from studies pertaining to apopulation of statistical units.


Conditions for the use of non-parametric tests.
The single-sample case (the binomial test, the chi-square goodness-of-fit-test, the Kolmorogov-Smirnov test).
The case of one sample, paired replicates (McNemar change test, the signed ranks test, the Wilcoxon signed ranks test)
Two independent samples (the Fisher exact test, the median test, the Wilcoxon-Mann-Whitney test)
k independent samples (the extension of the median test, the Kruskal-Wallis one-way analysis of variance by ranks , the test for heterogeneity of variance with k samples)
Non-parametric tests for k dependent samples (the Cochran Q test, the Friedman two-way analysis of variance by by ranks)

Assessment methods and criteria

There is no exam

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