People have been raving about this using obvious examples and conceptual approaches and I still can’t see how to explain this.
I am working on a customer feedback datasheet and I have binary data I wish to correlate to a Net Promoter Score.
Statistical importance methods I have implemented, such as Shapley values on an OLS model and Jackson’s Relative Importance on an multinomial ordered logistic regression model have both agreed and given me a sketch on what are the variables that influenced the outcome the most.
The p-tests I have though I find hard to interpret in words I can explain to a customer.
Say for instance Shapley And Jackson both concluded that the waiting time is causing a low NPS outcome by having a high negative weight, I can go on and directly conclude that the people who complained about waiting time gave you people low scores.
But when the p-test comes in with a high p-value of like 0.3. What does that even mean? Why is it important according to Shapley, Beta coefficients, and Jackson, while its p-test is essentially saying this might as well be luck? How do I explain that to someone who just wants to know what’s wrong with his service?