the amount of evidence provided by the observation of a green apple to be zero, when it is in fact non-zero but extremely small. I. J. Good's presentation of this argument in 1960 is perhaps the best known, and variations of the argument have been popular ever since, although it had been presented in 1958 and early forms of the argument appeared as early as 1940. Good's argument involves calculating the weight of evidence provided by the observation of a black raven or a white shoe in favor of the hypothesis that all the ravens in a collection of objects are black. The weight of evidence is the logarithm of the Bayes factor, which in this case is simply the factor by which the odds of the hypothesis changes when the observation is made. The argument goes as follows: ... suppose that there are objects that might be seen at any moment, of which are ravens and are black, and that the objects each have probability of being seen. Let be the hypothesis that there are non-black ravens, and suppose that the hypotheses are initially equiprobable. Then, if we happen to see a black raven, the Bayes factor in favour of is i.e. about 2 if the number of ravens in existence is known to be large. But the factor if we see a white shoe is only and this exceeds unity by only about if is large compared to . Thus the weight of evidence provided by the sight of a white shoe is positive, but is small if the number of ravens is known to be small compared to the number of non-black objects. Many of the proponents of this resolution and variants of it have been advocates of Bayesian probability, and it is now commonly called the Bayesian Solution, although, as Chihara observes, "there is no such thing as the Bayesian solution. There are many different 'solutions' that Bayesians have put forward using Bayesian techniques." Noteworthy approaches using Bayesian techniques (some of which accept !PC and instead reject NC) include Earman, Eells, Gibson, Hosiasson-Lindenbaum, Howson and Urbach,<ref>Howson, Urbach, 1993 Scientific Reasoning: The Bayesian Approach, Open Court Publishing Company</ref> Mackie, and Hintikka, who claims that his approach is "more Bayesian than the so-called 'Bayesian solution' of the same paradox". Bayesian approaches that make use of Carnap's theory of inductive inference include Humburg, Maher, and Fitelson & Hawthorne. Vranas