Recognizing the importance of choosing and comparing models / concepts might be a prerequisite concept. People learn this in various ways … When it comes to choosing what parameters to include in a model, statisticians compare models in various ways. They care a lot about predictive power for prediction, but also pay attention to multicollinearity for statistical inference. I see connections between a model’s parameters and an argument’s concepts. First, both have costs and benefits. Second, any particular combination has interactive effects that matter. Third, as a matter of epistemic discipline, it is important to recognize the importance of trying and comparing frames of reference: different models for the statistician and different concepts for an argument.
Recognizing the importance of choosing and comparing models / concepts might be a prerequisite concept. People learn this in various ways … When it comes to choosing what parameters to include in a model, statisticians compare models in various ways. They care a lot about predictive power for prediction, but also pay attention to multicollinearity for statistical inference. I see connections between a model’s parameters and an argument’s concepts. First, both have costs and benefits. Second, any particular combination has interactive effects that matter. Third, as a matter of epistemic discipline, it is important to recognize the importance of trying and comparing frames of reference: different models for the statistician and different concepts for an argument.