The EU-funded 'Algorithms for testing properties of distributions' (Distribution testing) project is examining the most efficient ways of understanding probability distribution. It is studying the complexity of samples and the time needed with respect to distributions over a large area or domain. Analysing sample distributions and random variables in such cases has traditionally been complex and challenging. Thus, the main objective of the project is to develop different mathematical and computer-generated algorithms that can study distributions and probabilities in better ways. The project team is closely scrutinising different kinds of distributions to develop these novel algorithms. Numerous tests are being conducted and the detailed observations are being documented. Distribution testing is also probing previously unstudied properties, as well as the relationship between computational complexity and sample complexity, to reach its aims. These new algorithms will shed light on emerging applications in data mining and natural sciences. They will facilitate this angle of statistics and support new sets of data and variables in research and in multiple environments.