A learning environment for statistical education aims at providing on-line course material for distance learning. It typically also includes practical exercise material, providing the student the ability to test his/her statistical knowledge in a real-life situation. A desirable aspect of a self-test module is that students cannot cheat and copy the answers from their colleagues. The Elestat project maintains this scenario with the construction of a web-based self-test module that contains many realistic datasets of which the observations are randomized before they are presented to a student, obliging the student to analyze a different dataset each time an exercise is started. The student is guided through the exercises in a step-by-step manner. The open source statistical software package R takes care of calculating the correct solution in real-time and offering immediate feedback. The web-technology is based on a collaboration between Java and the Rserve-interface. The Elestat project is accessible via the website http://www.Elestat.be. INTRODUCTION A growing number of electronic learning environments have begun their march through the field of education. Many e-learning environments for statistical education aim at providing on-line course material for distance learning (Graham et al., 2000, and Stephenson, 2001). Some examples of this material consist of theory content, example exercises, applets and self-tests. Rather than only designing static learning content, the student must be provided with a tool to test his knowledge on the subject. It is hard to work out a self-test module which bans the opportunity to interchange correct answers between students and thus resists cheating and avoids repetition of questions. Moreover, a typical self-test consists of a database containing many multiple choice questions from which a random sample is taken and presented to each individual student (McLeod et al., 2003). If the generated database is sufficiently large, this structure will indeed solve the problem of students interchanging correct answers. But what if an enthusiastic student wants to make use of the self-test very frequently? The probability that this student gets the same question more than once is surely present. In statistics, however, exercises with only multiple choice questions are not always appropriate. A very important topic in the field of applied statistics is the analysis of datasets. Each analysis includes the calculation of data-dependent quantities (e.g., test statistic, p-values, means, confidence intervals). Apart from multiple choice questions, where the correct computed value is listed among the selectable choices, open questions where the student needs to fill out his calculated values, are highly desirable. Although this type of questions may be very good, they still suffer from the drawback that they are not of interest anymore to the student when presented a second time. In this paper, we introduce a web based self-test for statistical data analysis, which generates for every student a randomized dataset. The most important advantage of this approach is that even when a student is provided twice the same problem with the same questions, the data analysis will be different each time. As a consequence, the correct conclusions are possibly different too. This self-test tool is a part of a larger e-learning environment (www.Elestat.be). At Ghent University, the exercise tool has been used in a basic statistics course for students 3 rd Bachelor in bio-engineering sciences. The first section contains a more detailed discussion about the setup of such an electronic exercise environment. In the next section, some technical specifications of the random generator and navigation tools are given. Finally, the results of a survey are discussed in the final conclusion.