How to design a study that survives peer review — Part 1: It starts before you collect any data
This is the first post in a series on designing research that holds up under scrutiny. Over the coming weeks we'll move from the research question through to responding to reviewers. If you're preparing work for publication — or building a research portfolio for a Global Talent or EB-1 application — this series is for you.
Most papers are not rejected at peer review because the writing was poor or the topic was uninteresting. They're rejected because of decisions made before a single data point was collected. By the time a reviewer sees your manuscript, the fatal flaws are already baked in — and no amount of polished prose can rescue a study that was designed to fail.
So this series starts where good research starts: at the design stage. Get this right and peer review becomes a formality. Get it wrong and you're writing a resubmission before you've finished the first submission.
The question is the foundation — and it's usually too big
The single most common design flaw is a research question that's vague, unanswerable, or trying to do too much. "How does diet affect health?" is not a research question; it's a research career. Reviewers can spot an unfocused question immediately, because everything downstream — your methods, your sample, your analysis — inherits its fuzziness.
A strong question is specific, answerable with the resources you have, and honest about its scope. Narrowing it feels like shrinking your ambition. In fact you're making your study possible — and a narrow question answered well beats a broad one answered badly every time a reviewer is deciding your fate.
Know your hypothesis before you know your result
State what you expect to find, and why, before you collect data. This isn't bureaucracy — it's the line between confirmatory research (testing a prediction) and exploratory research (generating one). Both are legitimate, but reviewers are unforgiving when exploratory findings are dressed up as confirmatory ones. Deciding which you're doing, in advance, protects you from that charge.
Design the analysis before you design the data collection
This reverses how many researchers work, and it's the habit that most reliably survives peer review. If you know exactly how you'll analyse your data, you'll know exactly what data you need — and, just as importantly, what you don't. Researchers who collect first and figure out the analysis later almost always discover a gap too late to fix: a missing control, an underpowered sample, a confounder they never measured. A reviewer will find that gap. Better that you find it first, on paper, when it costs nothing to fix.
The reviewer is already in the room
The mindset that changes everything: imagine your harshest reviewer sitting beside you as you design the study, asking "how do you know that?" of every choice. That question — asked early, of your question, your hypothesis and your analysis plan — is the whole discipline of good design in miniature. It's the same standard, incidentally, by which endorsing bodies judge a research portfolio: not how impressive the topic sounds, but how sound the reasoning is.
Coming next
In Part 2, we'll tackle the piece reviewers scrutinise hardest: sampling and statistical power — how many participants or observations you actually need, and why "as many as I could get" is the wrong answer.
Dr Abimbola Folami is a PhD researcher and Member of the Royal Society of Biology. Bimteck offers research consulting in biotechnology, microbiology and computational biology — learn more.