Most readers might consider this discussion a waste of time as it leads nowhere specifically. At first glance that view could be justified. But a closer look reveals deep insights into how much our peers do and do not understand about our general method of science. The distinction between an hypothesis and a research question can be sometimes as described by two authors above Ahmed and Mohammed. But they incorrectly formulate these terms in the context of the scientific method. Most readers would also be unaware of the hypothesis testing theory that underpins the term 'hypothesis', and by implication the term 'research question'. It is not up to us to redefine these terms, as testing theory already does this very well. So that is why a discussion of these terms is important, because it can reveal much about what we don't know about the general method of science. For example an hypothesis is a logical proposition, not a prediction or an explanation. Predictions and explanations are only possible if there is a developed theory to which to relate any observations. An hypothesis does not require the pre-existence of a theory, nor does a research question. A theory usually only emerges later after finding patterns among observations and after testing many related hypotheses. In most fields of social science we have an abundance of theories to choose from in order to describe and explain any observations about anything. The error of two authors above reveals a lack of knowledge of hypothesis testing theory. The proposed (not predicted) relationship between two variables cannot be tested directly as the hypothesis testing theory and its statistics do not permit that. Only the null hypothesis can be tested statistically, and then, it can only be rejected or not. Finding there is no relationship between two or more variables, or that there is 'not no relationship' (the null hypothesis is false) is not the same as finding variables A and B (or C,D, E) are related. Students of science unfortunately do not usually learn this early enough. Most only learn about these critical theoretical issues at Masters level or higher. Why does that matter to the methods of science? The key assumption in the general method of science is that we must be able to falsify an hypothesis in theory, and in practice it must be either falsified or not. If an hypothesis cannot be falsified, and no experiment could be designed to test that, some would argue it is meaningless in science. The method requires that we do not try to prove an hypothesis, but instead we try and reject it. That means we need to be mindful that many forms of bias can influence how hard we try to do that. For example in a randomised control trial (RCT), we may include a control intervention that is so low in intensity, or so poorly implemented, that even the weakest of alternatives would be more effective. So to avoid bias we need to set up fair contests between plausible and strongly designed alternative interventions. We need a fair race, so every horse in the race should have a fair chance and not be nobbled in some way in advance through a biased design. That is not often done well in the social science because there is pressure to obtain positive results for the favoured outcome (usually H1). That means there can be unconscious pressure to encourage failure of the control, or to not be so rigorous in its implementation and measurement. This is often the case in social sciences, where even in the best RCTs often the control (treatment as usual or an alternative intervention) is often not described as well as the experimental condition. If it is not described as well, how do we know its intensity and fidelity were actually sufficient to give it a fair chance? Even with several systems around to guide the design of high quality RCTs, there is still a lot of unfairness hidden in many studies that do not run a fair race, but report it as if they did. So one of the first things I do when reviewing an RCT paper is read how the control intervention was designed, described, implemented and measured, and compare that to descriptions of the experimental intervention. Sometimes in science the most naive questions or statements can reveal much about what we know and don't know about our scientific method. In an age where media and politics can distort reporting in science, we need to work harder at designing even better studies that really do contribute to theory development. Even this brief discussion of what is an hypothesis and what is a research question, can open the doors to a much greater depth of knowledge. We owe it to the public that funds science to do a better job with the money they entrust to us. Answer from Geoffrey Rolland Waghorn on researchgate.net