Why might one avoid negative variables in modeling?

Avoiding negative variables in modeling is often recommended because they can introduce complexity and confusion into the analysis. Negative values can make interpretation difficult, especially if the context of the variable is not clear to all stakeholders. For instance, in a business context, a negative sales figure might imply a loss, but it could also cause misinterpretation if the audience is not aware of the underlying reasons for that negative value.

Additionally, negative variables can complicate statistical analyses, especially in models where assumptions about the distribution of data are critical. Many statistical techniques are designed with positive data in mind, so using negative variables may violate these assumptions and lead to unreliable results.

Overall, avoiding negative variables helps ensure that the model remains interpretable and that stakeholders can draw clear conclusions from the data.