Gradual patterns mining aims to extract from numerical data, the frequent covariations between attributes of the form "The more/less x1, ..., the more/less xn". It is significant for capturing the variability of numerical values in applications when the volume of data becomes large. Moreover, statistical correlation, which highlights relationships between variables, can also be used to express covariations in numerical data. Although gradual patterns and statistical correlations capture covariations from numerical data, gradual patterns provide more expressive knowledge. To our knowledge, no work in the literature focused on studying the differences between these two concepts to highlight the limits and the advantages of gradual patterns regarding statistical correlations. In this article, we conduct a comparative study between the gradual patterns extracted using different semantics of graduality and statistical correlations, presenting the similarities, differences, advantages and disadvantages of each of the concepts for numerical data processing. This study is completed by experiments carried out on several numerical databases, the results of which confirm the contribution of gradual patterns compared to statistical correlations.