In statistics generally the model that has the least variables and is the most statistically probable is the one used. See things like AIC or Bayesian Information Criterion on how to choose a good model. This means that Occam’s razor is accurate. Given that is is possible to continuously add variables to a model and get a perfect fit but have the model be blown apart with the addition of an additional observation that is not otherwise influential, then, unless you are defining probability to include an Information Criterion, your formulation is less useful.
In statistics generally the model that has the least variables and is the most statistically probable is the one used. See things like AIC or Bayesian Information Criterion on how to choose a good model. This means that Occam’s razor is accurate. Given that is is possible to continuously add variables to a model and get a perfect fit but have the model be blown apart with the addition of an additional observation that is not otherwise influential, then, unless you are defining probability to include an Information Criterion, your formulation is less useful.