Archer taking aim at a target

7 – In grant applications, state your hypothesis and your a priori

Instead of a vague ‘effect’, state which readouts you will test and whether you expect an increase or a decrease


Would you give 300,000$ to someone who aims to “Study the effect of DNA in the brain?” Probably not – it’s much too vague, right? Yet many funding applications present scientific aims that are almost as vague, compromising their chances of funding. Conversely, if your aim is sharply defined, you will stand out from other candidates. Let’s see how to achieve this.

[NB: this post applies to projects that test hypotheses, i.e. to most fundamental projects, but it'll also be useful if you present applied projects – many of which include at least one hypothesis].


1) Avoid these overused, vague words: ‘role’, ‘impact’, ‘effect’...

Researcher walking into a lab
My scientific aim is to go to the lab and do experiments.


When I started training researchers to design funding proposals, I was puzzled because I often encountered statements I could not understand. Researchers wrote that they would:

Determine the impact of X on Z
Determine the role of X
Determine whether Z is involved in X
Determine how X affects W
Study the function of X
Determine the importance of X in Z


It took me a while to figure out why these statements are unclear. There are two reasons for that:
  • They don’t correspond to terms of the scientific method. You should aim to determine whether something causes, activates, regulates, is necessary, is a limiting factor, etc. The ‘Importance’ of a phenomenon is just not a scientific concept.
  • They are vague and don't help reviewers understand what exactly will be tested in the proposal. Which is the topic of the next section.


    2) Instead of vague aims, state your hypothesis and readouts

    Research funders, especially for fundamental research, love hypothesis-based research and dislike ‘fishing expeditions’ in which they have no idea of what can be expected.

    This often surprises junior researchers because funders claim that they want innovations, of which fishing expeditions are a primary source. After all, the Viking Leif Erikson (re)discovered America precisely during a fishing expedition. But in fact, though they do not say it explicitly, most funders prefer incremental innovations – testing one hypothesis at a time, so to speak.

    Therefore, you should be careful to clearly present a hypothesis, for example:

    Determining whether X promotes differentiation of Z
    Testing whether Z delays the appearance of X


    Viking Leif Erikson being judged by a tribunal
    Mr Erikson, we won’t fund your fishing expeditions again.
    Your discovery holds no interest.


    "But... I have no hypothesis"
    That may be true if your project is applied, rather than fundamental. In which case it’s perfectly OK not to have a central hypothesis.
    But it’s unlikely to be true for a fundamental project. Granted, in my courses, researchers frequently insist that they just want to “study the role of X on plasmas”, without any hypothesis as to what this role might be. Yet when I ask them what their readouts are, i.e. what they will measure, they say that they will look at the plasma stability, coherence, and vibration.
    But wait a minute - this means that they do have a hypothesis: they think that X will probably affect the plasma stability, coherence, or vibration. In other words, which readouts you use tells you what your hypothesis is!
    So next time you think you have no hypothesis, consider that you necessarily have at least some preconceptions, which you can easily identify by examining your readouts.


    "I get it, but I really have no hypothesis – in fact the main strength of my project is to use an unbiased technique"

    In that case, I have bad news for you. Using an unbiased technique might be a genuine scientific strength of your project, but most funders won’t see it that way, again because of the lack of a hypothesis.

    Consider the advice given by Jonathan Chernoff, who managed to keep his lab funded for 30 years:

    "Unbiased screens are the wellspring of discovery; never propose one."

    (How to get and keep your lab funded, MBoC 2018).
    I know. I feel your pain. But the sad truth is that you probably shouldn’t present an unbiased screen as a central element of a funding application, and certainly not as a first objective (because if the screen fails, so does your project).
    If you must include an unbiased screen, you should demonstrate that you have at least some idea of what to expect, ideally because you have done a similar screen in the past. For example, you could write “Based on our experience [reference], we expect to identify at least 30 candidates.” Giving (realistic) numbers reassures funders that you know what you do.


    Ok, back to our sheep, as the French say (i.e. let's get back to the main topic. I know, it sounds like a Welsh, Irish or Kiwi proverb ;-). When composing your hypothesis, you should also state your a priori. Let’s see why.


      3) State your a priori – do you expect an increase or a decrease?

      Researcher surrounded by angry smokers deprived of cigarettes
      No, I have no a priori – subjects may be either happy or angry when I deprive them from cigarettes.


      Many proposals contain this kind of aim:

      We’ll study how our candidate drug X affects the immune response

      The vague verb ‘affects’ annoys reviewers, who are busy people and would like to be immediately told whether the candidate drug is meant to activate or to inhibit the immune response. (This point is obvious for applicants, but reviewers cannot guess it…).

      So you should always make your a priori clear, i.e. which way you think the effect will go:

      We’ll study how our candidate drug X activates the immune response


      Applicants almost always have an a priori, since otherwise they wouldn't be in a position to apply for a grant. For example, they are unlikely to have a credible candidate drug if they don't have enough preliminary data to know whether it should activate or inhibit the immune response...


      "I’m afraid of stating my a priori – what if my hypothesis is wrong?"
      Some researchers think that stating their a priori too bluntly is risky, as it might be wrong. In fact, reviewers are perfectly OK with your hypothesis being wrong (that's the definition of a hypothesis, and remember, reviewers love hypotheses), on two conditions:
      1. Your project should have the potential to conclusively prove whether the hypothesis is right or wrong. In other words, it should be able to settle the matter, be published, and thus advance the field.
      2. Testing the hypothesis should not be your first objective (i.e. it should not be done in the first work package), because if the hypothesis is wrong, the project will stop there. Therefore, you should structure your project so that the hypothesis is tested only in objective 2 or 3.


        In summary

        OK, let’s start from a typical vague scientific aim and see if we can convert it into a clear aim using the 3 tips given above:

        Characterize the role of ZAK signaling in blastoma chemoresistance.

        As you can see, this aim contains no hypothesis or readout – only a vague term, ‘role’, is stated.

        Vague scientific aim without hypothesis, readout or a priori

        Now let’s include a hypothesis, the direction of the effect you expect (i.e. your a priori), and precise readouts:

        Determine whether ZAK signaling increases blastoma chemoresistance, proliferation or migration.

        Precise hypothesis with readouts and a priori

        Can you see how much clearer this sounds to reviewers? Having a precise aim creates a favorable first impression on the funding panel and greatly increases your chances of success.


        You may have noticed that once again, the advice above follows Maeda’s simplification algorithm. That is, you should remove the meaningless (i.e. vague aims) and replace it with the meaningful (i.e. your hypothesis, your readouts, and the direction of the effect you expect).

        [If you haven’t read the post about Maeda's algorithm, I highly recommend you do so now, as the procedure it describes is central to all aspects of communicating your research: designing a research funding proposal, but also scientific articles, talks, CVs, paper figures, talk slides…]


        Actionable points

        --> Take a look at your research proposal(s) and check these 3 points:

        1. Do you use vague words for your scientific aim(s), such as ‘function’, ‘role’, ‘impact’, ‘effect’, ‘importance’, ‘affects’, ‘is involved’ ?
        2. Do you state a precise hypothesis? Is it presented in the summary? Does it have a devoted heading?
        3. Do you indicate your a priori, i.e. which direction you anticipate (an increase, decrease, etc) for the effect you will investigate?


        If you have replied 'No' to the first and ‘Yes’ to the others, congratulations! In some fields such as biology, almost every proposal fails the checks #1 and #3. (If you were wondering, the most precise researchers tend to be physicists. But even they can still present a pretty vague overall aim in the title of their application).


        I hope the above advice is useful. I think it is key to writing competitive funding proposals, yet I have never come across it in 10 years of reading books and articles about research funding –  it is original to this blog.


        Have a nice day and fruitful research.


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