Caenorhabditis elegans is highly important in current research, serving as a pivotal model organismthat has greatly advanced the understanding of fundamental biological processes such asdevelopment, cellular biology, and neurobiology, helping to promote major advances in variousfields of science. In this context, the survival of a nematode under various conditions is commonlyinvestigated via statistical survival analysis, which is typically based on hypothesis testing, providingvaluable insights into the factors influencing its longevity and response to various environmentalfactors. The extensive reliance on hypothesis testing is acknowledged as a concern in the scientificanalysis process, emphasizing the need for a comprehensive evaluation of alternative statisticalapproaches to ensure a rigorous and unbiased interpretation of research findings. In this work, wepropose an alternative method to hypothesis testing for evaluating differences in nematode survival.Our approach relies on a clustering technique that takes into account the complete structure of survivalcurves, enabling a more comprehensive assessment of survival dynamics. The proposed methodologyhelps to identify complex effects on nematode survival and enables us to derive the probabilitythat treatment induces a specific effect. To highlight the application and benefits of the proposedmethodology, it is applied to two different datasets, one simple and one more complex.