15 - Quantify your results in grant applications and papers

In grant applications, quantify expected outcomes; in papers, quantify results presented in  headings  

 

We’ve seen that to clarify grant applications and papers, you could use a powerful procedure, ‘Maeda’s simplification algorithm’. Among other benefits, it forces you to focus on what is meaningful for reviewers.

One of the most meaningful elements you can provide is numbers: how much of something do you intend to achieve or did you achieve? Therefore, whenever possible, you should provide numbers:

• In funding proposals, quantify your expected outcomes and present them in the Title and at the beginning of each work package. This helps reviewers verify that your project is realistic.
• In articles, quantify your results and present them in headings. This helps reviewers understand their relevance.

Let’s look at some examples.

 

1) Quantify your expected outcomes in funding applications

A funding body once asked for my opinion on the title of their funding application. Yes, funding bodies also require money and must also apply for funding from other organizations!

Here was their initial title:

Increasing the number of researchers who create a start-up based on their work

Here’s the title I suggested:

Doubling the number of researchers who create a start-up based on their work, within 5 years

You can see why the project with the second title has more chances of getting funded: reviewers immediately understand its precise objective (‘doubling’ instead of ‘increasing’) and the time frame you commit to. Instead of having to sift through the application to grasp what you’re proposing, they can simply judge whether it is realistic.

As you can see, you should quantify your project’s main expected outcome in the proposal’s title whenever possible. You should also quantify the outcome of each objective. Let’s explore why.

 

Quantifying expected outcomes helps reviewers judge whether your project is realistic

One of the most important roles of quantifying your expected outcomes is to help reviewers assess whether your project is realistic:

• If you provide appropriate orders of magnitude, they can see that you know what you’re doing;
• If you don’t provide orders of magnitude or if they're unrealistic, they may question your competency. 

Unfortunately, they may default to thinking you don’t know what you’re doing (trust me – I've worked in a funding body).

Here’s an example where the expected outcomes of each work package are quantified. I have underlined the orders of magnitude provided.

WP1: We will design biomaterials with a regular nanostructure by using organoM synthesis.

This will provide us with ~10-15 types of representative biopolymers with a structural cell ranging from 2 to 5 nm. In WP2, we will determine their electric response to odors.

WP2: We will identify candidate biopolymers that have the highest electrical response in among those identified in WP1. We will select the 3 most promising candidates and evaluate them in WP3

WP3: We will validate the most promising biomaterials among candidates identified in WP2, by testing them in 4 representative, real-life applications.

Notice that in these sentences, the applicants not only quantify the number of biopolymers they will synthesize and test, but also their expected characteristics, and for how many real-life applications for validation. This helps reviewers check that the applicants know what they’re doing.

NB: you should place such sentences at the beginning of each work package, for which they serve as a summary.

 

 

Quantifying expected outcomes also helps justify your budget

Another important role of providing numbers is to help reviewers assess whether your budget is warranted. If they think it isn’t, they might still fund your project, but reduce your requested budget. That hurts – don’t give them the opportunity by not providing precise numbers.

Ok, time to reply to the most common objections.

 

Common objections

“I don’t know the orders of magnitude I can expect for my outcomes; I was planning to find out after I get funded”

I hear this often from rejected applicants! The problem with that strategy is that it will most likely not get you funded. You must identify expected orders of magnitude before submitting your project. If that’s any consolation, this effort of thinking about expected outcomes is not wasted; it will help you get your project started faster once you receive funding.

 

“Ok, but my project does not lend itself to quantifying expected outcomes”

If you cannot quantify expected outcomes, I still recommend to give orders of magnitude regarding at least your methodology, so that reviewers can judge whether your project is realistic. For example, imagine that you want to identify and study obscure books written by the Jesuits around the world. Obviously, you do not know before how many lay dormant in libraries carrying out your project. But:

• you should still have an idea of the minimum number of dormant Jesuit books you can realistically identify (if you didn’t have that kind of preliminary data, you wouldn’t be in a position to propose a convincing project).
• you also know how many books at most you can study.

So you could write for example:

“Work package 1. Based on our preliminary data and on our experience studying ancient books, we expect to identify between 5 and 12 dormant Jesuit books. We will select the 5 most interesting ones for in-depth textual analysis” 

 

“My project is exploratory, so I really cannot quantify expected outcomes”

Agreed. This being said, if your project is exploratory, you should not apply for traditional research funding schemes, because sadly, they no longer support exploratory research. Instead, you should either apply for proof-of-concept grants, or for organizations that fund exploratory research (e.g. foundations or local councils).

 

“Isn’t the current funding model stupid?”

It is – I’ve already covered this point and potential solutions in this post.

 

Now let’s see how numbers can also help you clarify your papers.

 

2) Quantify your results in the headings of your papers

To be clear and convincing, an article should present a chain of evidence. This is where the Results headings come in. Each main result (i.e. each link of the chain of evidence) should be introduced with a devoted heading. This enables readers to scan these headings and quickly grasp your article’s chain of evidence. 

Headings should include numbers whenever they help readers understand the strength of the effect you observed. Consider the following examples. Which one do you prefer?

X increases when Z is added

X doubles when Z is added

X doubles within 20 seconds when Z is added

 

You’ll probably agree that mentioning that X 'doubles' (instead of just saying it ‘increases’) is much more helpful to readers. Mentioning that this doubling occurs within 20 seconds might also be helpful if the timeline of the increase is relevant.

You can use numbers in many other situations. For example:

W remains constant

W remains constant (within 10%)

 

In summary, you should quantify your results in headings whenever it helps readers get a better idea of the magnitude of the effect you observed.