What should be done to ensure that the model is representative of the population it intends to predict?

To ensure that a model is representative of the population it intends to predict, considering outliers is crucial. Outliers are data points that deviate significantly from other observations in the dataset. They can skew the results and may provide misleading information about the overall trends and patterns within the data. By analyzing and addressing outliers appropriately, you can enhance the model’s accuracy and validity. This process often involves determining whether these outliers are genuine anomalies that should be investigated further or if they should be removed to prevent distortion of the model's predictions.

Adjusting sample size can also be important for model representation, yet it is more about increasing the reliability of results than directly addressing the issue of robust representation within existing data. Improving model choice helps focus on the algorithm used but does not inherently ensure that the model reflects the characteristics of the population it aims to predict. Evaluating model strength is essential for assessing its performance, but without first ensuring that the data is representative, even a strong model might yield inaccurate conclusions. Thus, focusing on outliers is a fundamental step in achieving a model that genuinely represents the intended population.