What is your question? This seems to be a statement of what you think the difference is between H1 (not H0) and an RQ. More correctly the hypothesis being tested is almost always H0, the null hypothesis, not H1. Both forms (H, RQ) are part of the general theory of research design and hypothesis testing. Different types of research use both forms at different stages, for different purposes. It is not always as simple as the text book makes out. A good question to ask is to what extent is randomisation assumed to be an essential part of H testing? And on a grammatical level, is it more correct to say a hypothesis, or an hypothesis?

Thanks Anita for introducing the philosophy of science underpinnings to hypothesis testing. As you say this is a complex world where the formal (Hypothesis testing of HO) is often blended with less formal (RQs). The mix often depends on the state of development of the relevant theory and the previous most related findings. Textbooks have enough of a challenge trying to clarify and impart basic concepts. Actual research in practice may begin with basic exploratory or descriptive goals, and move towards answering more specific questions, through to testing a null hypothesis against an active hypothesis. The latter involves some serious assumptions about the nature of the data, randomisation, and shape of distributions, governed by the requirements for statistical significance testing. This statistical method is based on a critical method of science, namely to find evidence that can falsify a pet hypothesis, (hence the link to philosophers of science) not to find evidence to support it. That is because the former involves less bias than the latter. Since everyone wants their pet theory to prevail, the discipline of science requires us to systematically look for counter evidence. As Anita points out this can involve a rich and diverse range of approaches and methods, from the very descriptive and exploratory to the very formal (namely testing if H0 can be rejected).

A hypothesis is a statement that prediction the relationship between variables, while a research question is a specific inquiry into a particular phenomenon or topic that a researcher aims to answer through their study.

A hypothesis often serves as the starting point for a study, offering a tentative explanation for an observed phenomenon. The purpose of the study is then to test this hypothesis by gathering data and conducting analysis.

A research question, on the other hand, guides the direction of the study and helps to determine the type of data that needs to be collected and analyzed. Research questions are typically more open-ended and focused on exploring a particular phenomenon or issue, rather than making a specific prediction.

In summary, a hypothesis is a specific prediction about the relationship between variables, while a research question is a broader inquiry into a particular topic.

I am amused by how hard some people try to fit the huge variety of activities that constitute research questions and hypotheses, into simple mutually exclusive categories. Did you not read Anita's answer above? It is not logically appropriate to try and do that. While the previous answers are not wrong they fail to capture the rich diversity of nuanced uses of both forms of enquiry. It is sometimes true to say that a RQ is an earlier stage of an investigation, and that an hypothesis is a more advanced stage. But, this is not a useful characterization because each has different purposes and contexts, and can take many forms. Neither of these are explanations as the previous author claimed. In its strict form the null hypothesis (H0) is the only one being tested. H1, H2, etc, are rarely directly tested. In fact under the theory of H testing they should never be tested directly. But there can be exceptions to everything. H0 is the key proposition stating that 'there is no difference between two or more groups'. The RQ form of this would be "Is there a difference between these two groups (on this parameter or set of parameters)?" Neither are sufficient as scientific explanations of anything. In fact they only differ by grammatical form. But it does help to understand formal hypothesis testing theory and how it is used in experimental sciences, versus the more narrative based investigations in qualitative investigations, which are better expressed as research questions. However, even these differences are not fixed because no matter what field of enquiry, it is possible to replace an RQ with an H, or vice versa. Sometimes it might take several Hs to get the same information generated from one RQ. Best to think of these as parallel languages of science that are more inter-changeable than mutually exclusive. Sometimes an RQ fits the situation best, sometimes an H. Sometimes an RQ is used to generate an H, or sometimes an H0 when falsified or not, generates a new RQ.

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.

Some time has gone past since these notes were added. I wonder if the authors who posed this question and who advocated for more simplistic answers, have given this issue more thought. It would be a sad day for science if no other readers cared about this issue, and did not use these notes to expand their knowledge of hypothesis testing theory and general method of science. If that is the case then the purpose of this function in Researchgate would remain vacant as well.