Research results are not the same as research claims. This is one of those distinctions that sounds simple but can trip researchers up. But understanding the difference can be really helpful.

Research results

Research results are what you get when you finish your data generation and analysis. They’re the direct outcomes of whatever inquiry you’ve been doing. Results are what comes straight out of your interviews, observations, surveys, experiments, or whatever method you’re using.

Think of results as having a very specific postcode. They live in the particular world you created through your research design. They know exactly which participants they came from, what questions were asked, how the analysis was done, and when it all happened. They’re like home-bodies who don’t like to venture too far from their original context.

Let’s say you’re studying doctoral students and how they navigate their first year. Your results might look something like this: “Seven of the ten doctoral researchers I interviewed described feeling overwhelmed by the transition from masters programmes to independent research” or “Students consistently mentioned struggling to understand what their supervisors expected from them during the first semester.”

These results are tied to your specific study, they are the your ten doctoral researchers, your interview approach, your analytical framework. They’re what actually emerged from your data generation process, not dressed up or extended beyond their immediate context.

You have to describe these results in your thesis or report, but you can’t stop there. You also need to make claims.

Claims are where things get interesting

Claims are a meaning-making process. Claims are where researchers start making arguments about what their results actually mean. Claims are your interpretation of what the results suggest beyond their immediate context. They’re where you go from “Here’s what I found” to “Here’s what I think this tells us about the topic.”

Claims involve making a leap, and like any leap, you might land gracefully or you might face-plant. The trick is being thoughtful about how far you’re jumping and being honest about the distance you’re covering.

Using the doctoral researcher example, you might make a modest claim like, “These findings suggest that the transition to independent research may be particularly challenging for first-year doctoral researchers.” That’s staying pretty close to your results. You can then link this claim to the existing literatures.

But you might want to go bigger: “Doctoral programs need to fundamentally rethink how they support students through the transition from Masters programmes to independent scholarship.” But that’s a much larger interpretive leap that extends well beyond what your ten interviews can directly support. Face-plant. Claims have to fit the research results. If you can find a truckload of literatures to support this larger claim, you are on more secure ground.

The art of interpretation

Now the tricky thing is that there is no formula for moving from results to claims. No recipe. No blueprint. It’s fundamentally an interpretive process, which means you’re making choices about how to frame your findings, what to emphasise and how far to extend your interpretive reach. You move beyond a straightforward description to something much more interesting.

This interpretive work is where a lot of the real intellectual labour happens in research. Your results rarely come with their meaning stamped on them. You have to figure out what they’re telling you, how they connect to existing knowledge and eventually, what implications they might have. It’s creative work, but there’s rigorous thinking involved.  Interpretation needs to be done thoughtfully.

And different researchers looking at the same results might develop different claims. Let’s imagine two people looking at the ten doctoral researchers. Both see structure and emotions are involved. But maybe one researcher emphasises the emotional aspects of what those doctoral researchers shared, while another focuses more on the structural issues they identified. Both might be drawing defensible insights from the same data generation process. It depends in part on what the researchers were looking for in the first place, it depends on their research questions.

Being transparent

All researchers need to be upfront about their interpretive moves. Instead of pretending that claims naturally emerge from results, examiners and informed readers require us to be explicit about our reasoning. Why did you interpret the findings this way? What assumptions are you making? How did your theoretical framework shape what you saw in the data?

This transparency isn’t about admitting weakness. It’s about being intellectually honest. When you acknowledge how your own position and assumptions influenced your interpretation, you’re giving readers the context they need to evaluate your claims thoughtfully.

It also means being honest about the extent (limitations) of your claims. Maybe your findings about doctoral researchers apply mainly to those in your particular discipline, or at research-intensive universities, or during the specific time period when you did your data generation. Good researchers spend time talking about where claims might not apply as well as where they do.

Understanding the results-claim difference

Understanding the results-claim distinction can actually make your research life easier.

First, it helps you be more strategic about how far to push your interpretive claims. You don’t have to make grand theoretical statements if your results don’t support them. Sometimes the most honest thing to do is make modest claims and call for more research.

Second, it gives you a framework for reading other people’s research more critically. Instead of just asking whether you believe their results, you can also ask whether their claims seem well-supported by those results. Are they making reasonable interpretive moves or are they stretching their findings too thin?

Finally, it helps you communicate your research more effectively. When you’re clear about the difference between what you found and what you think it means, your readers can follow your reasoning and engage with your work more productively.

And just for doctoral researchers…

Now, if you’re a doctoral researcher reading this, you’re probably thinking about how this applies to your own work. Maybe you’ve got some results that feel important but you’re not sure how far you can push your claims. Or maybe you’re looking at a pile of interview transcripts wondering what story they’re supposed to tell.

Here’s the rub. This is A Good Thing. The tension between results and claims is productive. It forces you to think carefully about what your data generation process has actually given you and what interpretive work you need to do to make it meaningful. It’s not a problem to solve; it’s how knowledge building works.

Your goal isn’t to eliminate the interpretive dimension of research but to do it thoughtfully and transparently. Your results provide the foundation, and your claims build the structure of argument and meaning that makes your research contribute to broader conversations.

The relationship between results and claims reflects something fundamental about how research works – research is not just about generating a load of data and reporting what you have amassed. It’s about the creative, interpretive work of figuring out what your results mean and how they connect to larger questions and concerns. When you’re transparent about your interpretive moves and thoughtful about how far to extend your claims, you’re contributing to the kind of scholarly dialogue that actually advances knowledge.

So next time you’re wrestling with the gap between your results and what you want to claim about them, remember that this gap is where some of the most important intellectual work happens. The trick is crossing it thoughtfully.