A collaborative filtering recommendation system typically involves recommending objects and items that meet the user's needs and expectations. Recommendation systems are of paramount importance because it guides the users to search their items, in which these items are right for themself. However, most of the recommendation systems are limited to using the measures between the users or between the items based on the pairwise ratings value, without considering relationships between one user’s rating value and all other user’s ratings values. In this article, we propose a collaborative filtering recommendation system, this system integrated with the t-Test distance correlation to measure the dependence between the random vectors. The energy distance measures are based on certain Euclidean distances between sample elements. After explaining the proposed system, the evaluation is performed on the Jester5k database with two experimental methods, when data is partitioned in two approaches: Split and Bootstrap. The results show that the Precision-recall value of the t-TestCF is always higher than the two compared collaborative filtering recommendation systems (these two recommendation systems are available in recommenderlab).