Which aspect of a model refers to the selection between different statistical models based on certain criteria?

The term that best describes the selection between different statistical models based on certain criteria is model choice. This aspect of a model involves evaluating various statistical models to determine which one best fits the data or meets the objectives of the analysis. Criteria for model choice can include goodness of fit, simplicity, interpretability, predictive power, and the ability to generalize to new data.

Understanding model choice is essential because the chosen statistical model can significantly impact the results and conclusions drawn from the data analysis. It is important to select a model that balances complexity and performance, ensuring that it accurately represents the underlying trends without being overly complicated or overfitting the data.

The other options, while related to data analysis in various ways, do not specifically refer to the process of selecting between multiple statistical models. Sample size pertains to the number of observations in a dataset, outliers refer to data points that differ significantly from other observations, and model strength typically describes the reliability or robustness of a model rather than the selection process. Thus, model choice is indeed the correct aspect that deals with the evaluation and selection of different statistical models based on defined criteria.