Let me give you a simple example of why the assumptions matter in a model though almost the only thing people pay attention to is the headline finding. Let& #39;s assume I run the following model: I + S = P where P = -10. I then tell you -10 is the number of pounds I forecast 1/n

Losing during corona quarantine. You report this around the world as Balding forecast to lose 10 pounds during corona quarantine. That& #39;s the only thing people pay attention to and the only questions that get asked. Is this a good forecast? Based upon the information provided 2/n

You can& #39;t answer the question if it is a good and reasonable forecast. What if I tell you now that I=ice cream and S=salad. Now we are getting some place. We are actually looking at what I might eat. Now what if I told you I set the ice cream parameter to 1 because I promise 3/n

To lose weight and salad to m-1 where m is all meals in quarantine except the one time I have ice cream. All of a sudden it makes sense why my forecast produces a 10 pound weight loss but it raises questions about three models assumptions. I mean ice cream only once 4/n

During the entire corona quarantine? That& #39;s just not realistic. The forecast is only as good as the assumptions. Anyone can make a forecast. It is vital trust we look at the assumptions trust make up there model to see how accurate the assumptions are that make up the model.