In-flight icing is a dangerous phenomenon that poses significant risks to aircraft safety, resulting from the accumulation of ice on surfaces due to supercooled water droplets. This ice formation can lead to reduced visibility, engine power loss, blocked probes and vents, and adverse effects on aerodynamics. Current methods for assessing icing scenarios rely heavily on costly wind tunnel and flight tests highlighting the urgent need for validated simulation tools. The existing reliance on experimental measurements as the sole "ground truth" in the validation process often leads to biased model assessments, compounding uncertainties that can adversely affect statistical reliability. This action aims to revolutionize the validation process of numerical simulations for in-flight icing by directly addressing these uncertainties and biases. I propose a novel statistical validation framework that incorporates uncertainties at every stage of analysis, enabling a clearer identification of physical model gaps. The proposed methodology encourages a dynamic and continuous exchange between experimental data and numerical models, allowing for mutual refinement. The research objectives include: (1) Characterizing uncertainties in experimental data related to ice accretion through focused campaigns, leading to reliable datasets; (2) Developing robust statistical metrics that comprehensively capture the variability inherent in both computational predictions and experimental outcomes; and (3) Benchmarking the application of these methodologies to evaluate existing ice accretion models. This innovative approach promises to bridge the gap between experiments and simulations, providing reliable tools for aerospace design and certification. If successful, this work will establish a new standard in certification by analysis, reshaping future validation practices in complex multi-physics applications such as combustion, heat transfer, and fluid-structure interactions.