I think you’re making this argument a bit strongly. Now, I’ve written a number of posts arguing that most people are too dismissive of flaws in models that only occur in hypothetical or unrealistic situations, but I don’t think perfection is realistic. It seems that a model with no flaws would have to approach infinite complexity in most cases. The only reason why this rule might work is eventually your model will become complex enough that you can’t find the mistake. Additionally, you will be limited by the data you have. It’s no good knowing that prediction X is wrong because you ignore factor F if you don’t have data related to factor F.
I think you’re making this argument a bit strongly. Now, I’ve written a number of posts arguing that most people are too dismissive of flaws in models that only occur in hypothetical or unrealistic situations, but I don’t think perfection is realistic. It seems that a model with no flaws would have to approach infinite complexity in most cases. The only reason why this rule might work is eventually your model will become complex enough that you can’t find the mistake. Additionally, you will be limited by the data you have. It’s no good knowing that prediction X is wrong because you ignore factor F if you don’t have data related to factor F.