Select the three correct statements about the Evaluation stage of the data science methodology.

Model Evaluation is a crucial stage in the data science methodology, focusing on assessing the performance and validity of the models developed during the earlier stages.

The first statement points to the importance of handling and interpreting data correctly throughout the evaluation process. This is essential because any errors in data handling can lead to incorrect conclusions or insights, impacting the overall reliability of the model. By ensuring that data is properly handled, practitioners can be confident that the model's performance metrics reflect its true capabilities.

While several aspects of model evaluation are important, statistical significance testing is not a requirement in every scenario, as it is primarily relevant in contexts where hypothesis testing is needed. Thus, it's not a universal component of the evaluation stage.

The statement regarding validating the model's design is also crucial; however, it is more specific to the architecture and goals of the model rather than the evaluation of its performance per se.

Lastly, ensuring that the model processes data as intended does relate to model evaluation, as part of assessing its operational effectiveness. This involves checking that the model can handle incoming data and provide outputs in the expected manner, which aligns closely with evaluating its practical application in real-world scenarios.

Overall, the correct statements focus on the integrity of data handling and the logical design of the