The purpose of this paper is to report on the conception and some results of a long-term<br>university research project in Budapest. The study is based on an innovative idea of teaching the basic notions<br>of classical and Bayesian inferential statistics parallel to each other to teacher students. Our research is driven<br>by questions like: Do students understand probability and statistical methods better by focussing on<br>subjective and objective interpretations of probability throughout the course? Do they understand classical<br>inferential statistics better if they study Bayesian ways, too? While the course on probability and statistics has<br>been avoided for years, the students are starting to accept the "parallel" design. There is evidence that they<br>understand the concepts better in this way. The results also support the thesis that students' views and beliefs<br>on mathematics decisively influence work in their later profession. Finally, the design of the course integrates<br>reflections on philosophical problems as well, which enhances a wider picture about modern mathematics and<br>its applications.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education