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Epidemiology Classical Methods for Data Analysis

Friday 6 November 2009

Many students find courses on statistics boring, which in many cases is the result of the challenging character of dry mathematical equations, compared to illustrative biomedical lectures. However, in every scientific discipline, whether biomedical or not, use of some form of statistics is inevitable. Although basic statistical knowledge is obtained during the bachelor of biomedical sciences, checking significance of data during your master internship can be quit challenging! My personal experience is that even most PhD supervisors or colleagues cannot provide the proper statistical feedback needed. In addition, many graduate students and even post-docs take crash courses in statistics, nowadays! Why? A lack in statistical knowledge may lead to loss in data interpretation and missing unseen results. How can you criticize scientific articles if you cannot distinguish ANOVA from the t-test, properly? When do you take the SEM and when do you to take the standard error? Multiple logistic regression, anyone? There is a very good solution. The course Classical methods in data analysis of the Master Epidemiology. This course starts with the basic applications of biostatistics in the analysis of medical research data. In confirmation with the stereotypical image of the Msc. Epidemiology, this course contains lectures on statistics and mathematical analysis.

However, the course is very innovative and focused on practical use of the statistical tests. By using example data that could very well be data from your future experiments, the statistics become much more concrete and understandable. In four weeks you will be able to apply the right tests and formulate the right questions. During the morning sessions, a series of lectures will discuss one topic per day, starting with 15min of summary of last day. In the afternoon, you will test your knowledge on the computer with SPSS and R software. The sessions are aimed on not just to understand-, but to get experienced with- statistical data analysis. This distinguishes the course from many other crash courses. It is practical, complies with the needs of master students and can actually be followed without any common knowledge of statistics. In addition, the inspiring atmosphere and adequate support from the many supervisors made this course outstanding! However, since epidemiology students have little more working experience with R software, I advise to get some information on this program before starting the course. This will spare time in the beginning. The course organization provides a map with sheets from all the lectures and course material, including self-study examples. The latter is the true key to test and strengthen your statistical knowledge. Be aware that this course only has limited seats for students outside the epidemiology master, so be sure to arrange application in time and position yourself being privileged and motivated to follow this course.

Your fellow student,
Jonas Kuiper