Conventional approaches to hypothesis testing often hinge on linear models like ANOVA, subjective decision-making regarding factors and their interactions, and reliance on in-sample inference without thorough evaluation on hold-out data. These oversimplifications, subjective choices, and limited generalizability may cast doubt on the relevance and validity of scientific theories and conclusions. A transformative shift towards more resilient methodologies entails the incorporation of interpretability techniques, sophisticated machine learning models, and advanced out-of-sample model evaluation methods. For instance, interpretability tools like SHapley Additive exPlanations (SHAP) offer insights into model predictions, providing a nuanced comprehension of individual factors and their interactions. Integrating complex, high-dimensional machine learning models, such as Gradient Boosted Decision Tree Models, enables the application of flexible, non-linear models that inherently account for factor interactions. Furthermore, advanced out-of-sample evaluation techniques like cross-validation offer a more realistic assessment of a model's generalization capabilities. Illustrating this innovative approach through examples in visual aesthetics and spider phobia research, we showcase how researchers can delve into intricate details and scrutinize the subtleties of how individual factors and their interactions impact model predictions. Additionally, computing straightforward yet interpretable effect metrics, coupled with statistical testing, adds a layer of credibility to the derived conclusions. In summary, this integrated framework circumvents many of the drawbacks associated with traditional hypothesis testing, laying the groundwork for a more robust and credible scientific inquiry.