In this study, we present a novel evaluation framework for image-captioning models that integrate statistical analysis with common evaluation metrics, utilizing two popular datasets, FashionGen and Amazon, with contrasting dataset variation to evaluate four models: Video-LLaVa, BLIP, CoCa and ViT-GPT2. Our approach not only reveals the comparative strengths of models, offering insights into their adaptability and applicability in real-world scenarios but also contributes to the field by providing a comprehensive evaluation method that considers both statistical significance and practical relevance to guide the selection of models for specific applications. Specifically, we propose Rank Score as a new evaluation metric that is designed for e-commerce image search applications and employ CLIP Score to quantify dataset variation to offer a holistic view of model performance.