It is important to approach the situation in a transparent and informative manner. Start by explaining the logic behind your choices, highlighting how the selected methods align with the project objectives and industry best practices. Use data and concrete examples to demonstrate the effectiveness of the chosen methods in similar contexts, emphasizing their advantages in terms of accuracy and validity of results. Additionally, be open to discussions and suggestions, showing willingness to adapt approaches if necessary, while always grounding your decisions in evidence and expected outcomes. This clear, data-driven communication helps build trust and understanding among all parties involved.

These are the basic steps: ? Clearly articulate how your chosen method aligns with key project goals (e.g., efficiency, scalability, or accuracy) and business priorities. ? Present concrete results with key metrics or case studies demonstrating the method’s effectiveness in addressing the problem. ? Acknowledge other methods considered and justify why they were less suitable for the current context. ? Transparently state the limitations of your chosen approach and explain the strategies used to mitigate potential issues.

Para defender mis métodos estadísticos ante comentarios de partes interesadas, comienzo por explicar cómo mis métodos cumplen con los estándares de la industria, brindando ejemplos de su uso en situaciones similares. Luego, presento casos de éxito donde enfoques similares generaron resultados positivos, demostrando la eficacia de la metodología elegida. Mantengo una actitud abierta a la retroalimentación; escucho críticas constructivas y adapto donde sea necesario, pero afirmo con seguridad mi experiencia, dejando claro que las decisiones metodológicas son cuidadosamente fundamentadas. Este equilibrio entre apertura y confianza ayuda a ganar el apoyo de las partes interesadas.