Alright, first off, there is not only positive evidence but also negative evidence. Some words make an email message very likely to be spam, some make it very likely to be real. Other words make a message very likely to be spam by their absence, while yet others have the opposite effect. For instance, if you research drosophila for a living and frequently correspond with colleagues about them, the presence of that term is almost like a password, because no mass-mail campaign will be able to customize their texts to your habits that well - it would destroy the economies of scale that make spam viable in the first place.
Also, performance of a filter cannot be measured with just one metric. Detecting spam is very easy, indeed trivial, if you simply classify everything as spam - but then the false positives (detecting real mail as spam) are intolerably high. Detecting nothing solves that problem, but then the false negatives (classifying bad samples as good) make your life miserable. A good filter must reach good values on both counts, which makes it much more complicated than just a super-sensitive detector of something.
So right from the start you have not just one 'List of bad words', but at least four lists, and not just one criterion, but at least two. So far, Bayesian filtering really is the simplest method that does this well. If you find better one, by all means let's hear it.