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Box plots serve as an invaluable tool in various industries such as healthcare, manufacturing, and finance, particularly during the exploratory data analysis phase of product development and quality control processes. They allow teams to quickly visualize the distribution of key performance indicators, patient health metrics, production yields, or financial figures across different segments, facilitating comparison between variantes de productos, treatments, or investment portfolios. When designing a new product, engineers might utilize box plots to analyze user feedback data, identifying which features consistently meet or exceed user expectations, while also pinpointing outlier responses that may require further investigation. Participation typically includes product designers, data scientists, quality assurance experts, and stakeholders who contribute to a comprehensive understanding of dataset variability and trends. This methodology supports informed decision-making by visually encapsulating summary statistics that drive design iterations or improvement strategies, thereby enhancing product outcomes and customer satisfaction. The box plot’s capacity to display outliers prominently allows teams to address anomalous behaviors or results, informing risk assessments and mitigation plans across project phases, from ideation through testing, ensuring robustness in both design and functionality.