What's the Right Amount of Preliminary Data for a Successful R01 Grant?
As an early career researcher wondering about preliminary data, the one question I’m sure is running through your mind is, “How much preliminary data is enough?”
When the question about how much is enough for a successful R01 comes up, it really comes down to how much you need to do in advance to be able to have a successful grant application and then actually proceed with the research.
To be able to answer that question, you want to start with understanding how to lay out your Significance section. And whether you’re conducting hypothesis-driven research or intervention-style research, you want to highlight feasibility.
Focus On Feasibility
No matter what style of research you plan on using for your R01 application, it’s really about demonstrating to your reviewers that you have early evidence of the feasibility of what you’re doing.
Reviewers want to feel confident that you have experience with the tools and techniques necessary to execute your proposed project. In the case of intervention research, it would be early evidence of the feasibility of the intervention or the acceptability of the intervention.
Support The Scientific Premise
The other side of this is having preliminary data that supports your argument for the scientific premise of what you’re doing.
As you are focused on your significance section, really laying out the argument for why your research needs to be done in the first place is to explain the importance of the problem.
This is where you synthesize existing literature in terms of what we know already about the large scientific problem in which your project is situated. One key point you want to make here is identifying the big scientific problem: here’s what we know, and here’s what we don’t know.
That latter piece represents the gap in knowledge you hope to fill with your proposed project. Again, this is based on existing literature and the conversations in the literature.
Where the preliminary data fits in is in the form of your overarching hypothesis for how to fill that gap of knowledge.
For example, in hypothesis-driven research, you have preliminary data that informs your hypothesis. If you’re not doing hypothesis-driven research, your preliminary data is informing your motivating research question at a very high level.
The idea is to explain that we have early promising evidence, and we think this is what’s going on here (aka your central hypothesis). Then, that preliminary data also informs the development of your specific aims because your aims are different ways to go about testing that central hypothesis and ultimately filling that gap in knowledge. So it’s all connected.
How Much Preliminary Data Do You Need?
When it comes to a concrete answer around how much preliminary data I need for an R01, the answer is…
It depends.
I know it’s annoying and frustrating not to have a specific answer, but it truly depends on the type of research you’re doing and what’s already out there in the literature. That literature is what informs your argument for the premise of your project and helps develop your aims.
Though there’s no concrete answer, a general rule of thumb for hypothesis-driven research is that you want enough preliminary evidence to inform each of your aims so that it’s clear that there is a strong rationale for each of your aims and what you are actually trying to accomplish.
Then again, when it comes to feasibility, you want to make sure when you are speaking to this aspect in the approach section that you have enough there to be able to convince reviewers that you’re not starting from scratch and you know what you’re doing.
If you found this helpful, I strongly encourage you to sign up for our free resource library. We have lots of tools and tutorials to help you write a stronger NIH grant that gets reviewers excited about the potential of your research idea. There are also lots of other tools in there to help you plan and prepare your next grant so that you are organized and ready to go.