The primary objective of pharmacovigilance is the early detection and characterization of safety signals, defined as information suggesting new potential causal association between a medicinal product and an adverse event requiring further investigation. The French pharmacovigilance system is characterized by a decentralized organization relying on Regional Pharmacovigilance Centres, which provide in-depth clinical and pharmacological assessment of adverse drug reaction reports before their transmission to health authorities. The individual assessment of spontaneous adverse drug reaction reports, combined with quantitative analyses of pharmacovigilance databases (disproportionality analysis), is a key tool for safety signal detection. However, data from pharmacovigilance databases must be interpreted with caution. Causality between reported adverse reactions and the suspected drug remains uncertain due to several limitations, including incomplete information, diagnostic uncertainty, and potential confounding factors. Furthermore, the number of reported cases cannot be used to estimate the incidence of adverse reactions in the general population, notably because of selective reporting and limited data on the actual number of exposed patients. Disproportionality analyses, which are widely used to identify drug–adverse reaction associations, are useful for hypothesis generation. However, they cannot establish causality or quantify risk and are subject to numerous biases. Therefore, safety signals should be considered hypotheses requiring a multimodal assessment integrating clinical, pharmacological, and epidemiological data. Rigorous interpretation of these data is essential to avoid misleading conclusions that could have public health implications. The purpose of this work is to develop an educational tool to support the understanding and interpretation of data and signal detection analyses performed using pharmacovigilance databases.