The class isn ' t bad at all . Lipp is such a sweet person , but the class is a tad boring . Not a lot to prep in my opinion , just read the material and pay attention in class . Basically the bare minimum and you should do fine . <| sep |> shit , that was fast . I can do it now , although mine isn ' t ready . Would you be willing to trade again to evolve mine tomorrow ? Mine just evolved . I ' m ready when you are . <| sep |> Perfect thank you ! <| sep |> The whistle hadn ' t blown ! <| sep |> Science isn ' t about perfection , it ' s about getting as close as possible . With that in mind , unless you ' re identifying an absolutely fatal flaw in a model something which completely discredits any notion that its outputs could be even broadly proximate to an expected and representative result your model is flawed doesn ' t actually amount to much of a criticism unless it ' s also paired with a better model . What I mean by this is , hrm . Imagine the world before pregnancy tests . Someone invents a test . This test is accurate 90 % of the time . That ' s not perfect a 10 % error rate is , in this context , actually a pretty galling thing . Out of every 10 users , one would be expected to get a bad result , and we ' re literally talking about human lives here ! However , in the absence of a superior test or model if 90 % is the best we ' re able to do then that ' s still pretty good , certainly good enough that merely criticizing it for falling short of 100 % accuracy isn ' t going to cut it . No better alternative ? 90 %' s good enough . Conversely , if this test were only accurate 45 % of the time if it gave out so many false positives and false negatives that most users would expect a bad result then this would be unacceptable , and we could dismiss it for its inaccuracy . I mean , for one thing , how do we even know this test is working in the first place , right ? Even a test which generated totally random results would get about half of them correct ! How do we know that the correct results aren ' t just false positivesnegatives which , through sheer dumb luck , got assigned to the right person ? Especially given how heavy the consequences are here , the fact that this test is wrong most of the time is a fine reason to dismiss it and its predictive ability . With all that in mind if you ' re going to go after a climate model , you need to either show that the whole thing is so totally screwed up that it fits into that second category , or you need to propose a better model . If you aren ' t doing either , you ' re just spinning your wheels .