Hypothesis testing is not strictly the domain of frequentist p-values; there exists hypothesis testing in different frameworks including the Bayesian paradigm. In Bayesian hypothesis testing you need to specify priors, but it is still a hypothesis test. Null hypothesis significance testing, which typically uses frequentist p-values, is a well-established method for testing statistical hypotheses and is still a dominant approach when teaching people how to do statistics. Broadly speaking, computational issues are not really a major barrier to using Bayesian hypothesis testing for most of the tests that people tend to do nowadays, it's just less well understood/known by practitioners. Also priors tend to make some people squeamish, not for any (my opinion) strong justification, but just because it's easier to hide behind the protection of the "standard method". It's more of cultural point than anything else -- standard p-value hypothesis testing is the norm and whenever someone steps outside of that, they will be questioned as to why. It's simply easier to stay within the norm (after all, it's not like staying within the norm is preventing people from publishing papers). More on reddit.com

Well, I guess you don’t. I often do experiments for the “let’s see what happens” factor; sometimes I need to get information to form a hypothesis in the first place. But, in the long run, hypotheses also help define for what you are looking and help define the methodology of analysis. Sure, you don’t need to hypothesize that the coin will flip heads 50 times in a row, but you might want to say drug B is more efficacious than drug A when compared to placebo. This is a different experiment than saying A has a longer half-life than B, etc. methodologies May be different to focus on those specific questions. Hypotheses help define endpoints as well as beginnings, and making them at the start helps avoid the statistical pitfalls of stats-fishing: where you get the data and then decide how to analyze it. Edit: Addendum- on a personal note, i do find it annoying when I’m reviewing articles and I notice that everyone proves their hypothesis correct. I wish people had the honesty and guts to admit In their papers that they were WAY off-base when they started. More on reddit.com

The research question is the over-arching query you want answered, your aim is how you expect your experiment to answer the question, you hypothesis is what you predict the results of your experiment will be. For example Does caffeine increase reaction time? By comparing reaction times of people before and after consuming coffee I aim to observe this. I hypothesize that participants reaction times will be lower following caffeine consumption. At least thats my understanding More on reddit.com

A hypothesis is your first expectation for a specific research question before doing the research. For example, you do research on the relationship between hours worked and tiredness, your hypothesis could be: there is a positive relation between hours worked and tiredness. This expectation does however need to be informed. You read up on previous research to do so. A theory on the other hand is a framework to explain a phenomenon. Its the scientific consensus based upon a whole lot of research. It being a framework to explain makes it useful to look at a theory to formulate your hypothesis. If the theory states that people normally get tired after working long hours, you use that in yoir hypothesis. A theory is however not a law, and thus there always has to be a small chance of it being proven wrong. So science requires to have its current knowledge to be checked every now and then. Thats one of the reasons to reconduct research on already researched topics. Aside from that redoing research can also help develop the theory or general knowledge on a subject further. Perhaps people do indeed get tired after working long, but your new research shows that this is influenced by something else. In short: A hypothesis is used for your specific research and you use theory to form one. A theory is the accumulation of a lot of research and explains the bigger picture And because our knowledge has to be kept up to date and corrected if wrong, you reconduct research thats already been done More on reddit.com