A lot of these "no results" are just bad statistics. When a test shows no result it does not mean there is no significant difference. It means that the test cannot reliably tell given the available data. Technically, this is because as you decrease the probability of a type I error (lower the p-value) you increase the probability of a type II error. To lower the probability of a type II error, and hence have null results actually mean there is no significant difference, you needs lots of hits. Under assumptions that are reasonable for most e-commerce sites (e.g. 5% conversion, you want to distinguish changes of 5%) you need about 100'000 hits. Very few people have this much data, and hence their inferences are flawed. You can do a lot better than A/B testing with better maths. More here:
http://untyped.com/untyping/2011/02/11/stop-ab-testing-and-m...