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

The hypothesis isn't what the scientist used to think, it's what they're aiming to test. Whether they personally believe it or not isn't really relevant, often you'll have a hypothesis you don't believe because you're specifically trying to disprove it. The point is about the way the experiment is constructed, to give it a single clear purpose, so then other scientists can discuss how well it tests that particular hypothesis. It prevents you from just testing a ton of different factors, getting huge amounts of whatever data you can find, and then just combing through the results to find anything that looks like a discovery. You have to know what your goal is before you start More on reddit.com

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