 My name is Ronald Gaskus. I currently work at Okru in Ho Chi Minh City, Vietnam. We are a group of six persons, six biostatisticians, and I am the head of that group. Biostatistician tries to prevent incorrect conclusions to be drawn from the data analysis. So we are like the controllers, you could say, of all the quantitative analyses that are done at Okru. So in a clinical trial, we want to see whether some new treatment works better than the existing treatment or maybe works better than no treatment at all. And it may be that the new treatment also has side effects or maybe it doesn't work at all. Another thing is that the clinical trial in general is very expensive. There are lots of regulations around clinical trials, not without reason, but this requires a lot of logistics as well. So you don't want to include too many patients. You basically want to include just enough patients to show that the effect works better. And that is something that is done in the sample size calculation, so finding out how many patients to include. And that is a typical job of a biostatistician. So a clinical trial is basically the basic example of an explanatory model. You want to explain the effect of treatment. But in an observational study, what is different from a clinical trial is that in general the sicker patients are more likely to receive a treatment. In both cases the purpose is explanation, the explanation what is the effect of the treatment. So explanation and effect or efficacy is basically the same. Whereas in a prediction model you want to find factors that help you predicting the outcome. Research in biostatistics is important because nowadays so many data are collected and it's very important to do a proper analysis of these data. And this also holds for the medical field. Many more data are collected nowadays and this will generate a lot of noise you could say. A lot of data is not relevant so it has become only more important to find methods that are better able to discriminate between signal, important information and noise. As a statistician we work really at the basis, the basic part of the scientific research. We don't work at the bedside you could say. But if you take translational medicine literally it's about translation of results into practice. I think statisticians also play a role because they know very well the structure behind the model and I think they are in general very good in explaining the results. In general what I like about doing biostatistical research is the translation of a practical problem into a statistical model. And then again the translation of the results to practice.