What are the characteristics of a good hypothesis?
It should be 1) binary (either-or), 2) Stated to disprove what you really expect 3) Clearly relate to an available metric 4) Lead to a logical secondary hypothesis, if necessary.
The biggest errors that I have seen in teaching and applying statistics are unclear and too-broad hypothesis statements. A statistical hypothesis is not the same as scientific hypotheses, which may be more conjectural in nature. A statistical hypothesis must give as clear an answer as possible in terms of probability of one of two things happening.
The mathematical design and validity depend on the answer to a simple, single question – how likely do our results indicate a non-random effect? Anything more than that cannot be answered. If the problem is more complex or multi-layered you need to use multiple hypothesis tests.
Setting it up to really disprove your expectation usually results in a better result in terms of confidence levels and helps to avoid bias in your own interpretation of the results. You must, of course have sufficient data of sufficient validity to be able to make the calculations. If your hypothesis and data aren’t really related, the results will not mean anything.
Finally, if one hypothesis cannot answer the total questions of the problem, they should be designed to lead to useful answers in a proper sequence that narrows down options and clarifies the needed statistics.