|Education level||Postgraduate / Master of Science|
|Course title||Introduction to Statistics|
|Independent teaching activities||Hours per week||ECTS|
|Coursetype||General setting course, skills development|
|Teaching and assessment language||English|
Upon completion of the course, the student will know how to apply methods from the basic statistics. He will be able to manage descriptive statistical measures for summary data and compare the results between populations using representative samples. Additionally, he will understand the uncertainty involved when a random estimation of populations’ parameter takes place and he will interpret and evaluate better the findings.
An additional objective of this course is the understanding of biostatistics methodology through practical guidance and use of the programming language R.
Upon completion of the course, graduate students will be familiar with:
- The main differences between the types of studies of comparing populations
- The appropriate summary measure of variables for quantitative and qualitative data
- The difference between a sample and the population from which it came
- The characteristics of the normal distribution and the difference from the asymmetric distribution
- The sampling distribution and the concept of standard error of the mean
- The methodology of hypothesis testing, the concepts of p value, the level of significance and confidence interval, the types of I and II errors
- The basic parametric and non-parametric tests through real examples in the health and life sciences
- In which data can be applied the survival analysis and how to conduct such an analysis
The course participants upon completion will be able to:
- Understand and compute the descriptive statistical measures that appear in the medical scientific articles
- Formulate and interpret graphs appropriately
- Calculate association measures such as mean differences, risk differences, relative risks, odds ratios and incidence rate ratios related reason and impact-rates
- Calculate the appropriate sample size of a survey
- Use the fast-growing and evolving R project as a tool for statistical analysis and the creation of elegant graphs
- Introduction to R
- Different types of data, quantitative and qualitative
- Summary measures for quantitative and qualitative data (practice in R)
- Graphs for quantitative and qualitative data (practice in R)
- The normal (Gaussian) distribution (practice in R)
- Measures of association:
mean differences, risk differences, relative risks, odds ratios and incidence rate ratios
- Confidence intervals for measures of association
- Hypothesis testing- paired and two-sample t-tests:
Mann-Whitney U test and Wilcoxon Signed Ranks test (practice in R)
- Hypothesis testing -tests for more than two samples:
ANOVA and Kruskal-Wallistest (practice in R)
- Tests for categorical variables:
χ^2, Fisher’s exact test, Mc Nemar’s test (practice in R)
- Survival analysis:
Log-rank test and Kaplan-Meier plots (practice in R)
- Power and sample size calculation
- Module summary
Teaching and learning methods – evaluation
|Teaching methods||Face to face
|Use of information and |
communication technologies (ICT)
|Module structure||Work Hours per Semester||Activity|
|Exercises (Online discussion fora)||20|
|Exercises (Study relevant papers)||20|
|Essay background work||64|
|Overall work for the course||250|
- Aho, Ken A.Β Foundational and applied statistics for biologists using R. CRC Press, 2013.
- Bland, Martin.Β An introduction to medical statistics. 3rd Edition. Oxford University Press, 2000.
- Crawley, Michael J.Β Statistics: an introduction using R, 2nd Edition. John Wiley & Sons, 2014.
- Daniel, Wayne W., and Chad L. Cross.Β Biostatistics: A Foundation for Analysis in the Health Sciences: A Foundation for Analysis in the Health Sciences. Wiley Global Education, 2012.
- Kirkwood B, Sterne J. Essential Medical Statistics, 2nd Edition. Wiley, 2003.
- MacFarland, Thomas W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R. Springer, 2014.