Write your research question
An environmental issue, a dataset you have opened, and a rough sense of the system. If the data is not open in front of you, this sentence cannot be written yet.
- One sentence, as a question or a comparison statement
- The indicator named, with its units
- The years and the places it covers
- No strategy, stakeholders or policy in it
Fifty words, and every one of them has to be checkable against a file.
Everything that follows depends on what you decide here, so write five versions and throw four away. The indicator and its units were somebody else's decisions: naming them is the difference between a question a reader can verify and one they take on trust.
Something almost nobody knows
The official teacher support material says so directly and gives its own worked example as a comparison statement. What it must do is make clear what the quantities are and how they relate, so that it guides an appropriate method. A full stop is allowed.
In the four areas of Queensland's Shark Control Program that set both nets and drumlines, from 1996 to 2023, did white sharks make up a larger share of the sharks caught by one gear than by the other?
A comparison of the share of the sharks caught that were white sharks, between nets and drumlines, in the four areas of Queensland's Shark Control Program that set both (Gold Coast, Sunshine Coast North, Sunshine Coast South and Rainbow Beach), 1996 to 2023.
On this route I strongly prefer the statement. "Did X fall because of Y" quietly promises that you can say why, and a downloaded series cannot: you controlled nothing. A comparison statement claims exactly what you did, which is compare two sets of numbers you selected.
Everything below is how we suggest you actually do it.
Three ways a question fails before you collect anything
All three are easy to fix while you are still writing the sentence, and costly once you have collected data for it. Read your question against each one.
Shade is cooler than sun. Water runs downhill. If everybody already knows the answer, there is nothing to find out and your background has nothing to establish.
Anything with an "and how does…" in the middle. One half will go unanswered, and an unanswered half is what the focus strand penalises.
An ambitious question with no method capable of answering it scores below a modest one done properly. Simpler and well executed beats complex and unfinished.
Two quick tests. Say your question to somebody outside the class: if they can tell you the answer, you do not have a real question yet. And can you see, today, what the last paragraph of your report says? If not, the question is too big.
Four parts, all present
Four things have to be in the sentence: what varies, what you measure, where, and across which years.
The first two are your lines from step 1. What varies is line 1, what people do; what you measure is line 2, what happens to nature. So the sentence always has the same shape: [what happens to nature] against [what people do], in [place], over [years]. It runs one way, from people to nature, and never the other.
Name the indicator, not the concept. "Soil compaction" is a concept. On this route you never decided how to measure it: you chose a column somebody else had already defined. The sentence should say which one. "Deforestation" becomes "annual net forest loss in hectares"; "sharks killed" becomes "the number of white sharks caught", because the column counts animals caught, and not every one of them died.
Then the fourth part: the boundaries. Which years, and which places. They are not decoration. They are the inclusion rule you will have to justify in step 4. They appear early because they promise what your answer covers.
One independent variable, and one dependent variable: several of either makes it read as an investigation that never decided what it was asking.
The thing you compare, or set against your indicator: two kinds of gear, the forest cover of a commune, the ammonium in a river. Not the year on its own: a quantity changing over time is one variable, not two.
The actual column, as its publisher names it, and what it is measured in.
The places the data covers, which may be narrower than the issue.
The years you will use, chosen for a reason you can state.
Keep the strategy out of it
Some advice recommends a two-part research question ending with something about stakeholder tensions. Do not follow it.
Your question is a promise about what your data will answer, and a column of sharks caught cannot answer a question about who wants the nets in the water and who wants them gone.
Criterion B (Strategy) asks only that your issue has a clearly stated and credible connection to your question. The connection belongs in your report, not in the question.
One exception, and it is line 3's strongest form: when the plan is what people do, it is already in your data as line 1. A marine reserve set against the water outside it, or nets set against drumlines, names the plan as a column. What stays out of the sentence is the argument about it.
Do nets or drumlines catch the larger share of white sharks, and how have disputes between the Queensland Government and its critics shaped the Shark Control Program?
A comparison of the share of the sharks caught that were white sharks, between nets and drumlines, in the four areas of Queensland's Shark Control Program that set both (Gold Coast, Sunshine Coast North, Sunshine Coast South and Rainbow Beach), 1996 to 2023.
Three drafts, and what each fixed
The real sequence the white shark example went through, including one mistake that only opening the file could catch.
No indicator, no units, no period, and no variation to explain. And "work" means bather safety, which this file does not measure: it counts animals caught, not bites or swimmers.
Operationalised (the variables can now be measured), and asking the wrong question of this file. Drumlines are also set far north, from Cairns to Bundaberg, where white sharks do not swim. So a statewide comparison compares places as well as gear.
The indicator is named, and it is a share, so two gears that caught different numbers of sharks can be compared. The comparison is stated rather than a causal claim. The gear is the programme's own decision, so the strategy and the independent variable are the same thing. And 2023 was not chosen at random: from 2024 the programme added new locations, daily servicing and new drumlines.
The lesson is in the middle card, and it is not that the question was badly written. It was specific, measurable and answerable. It simply let a second difference in beside the one it asked about: the nets and the drumlines were not in the same places. If your two groups differ in more than the thing you are comparing, your answer is about both.
From the file's Area column, read before any white shark was counted. Only four areas set both gears: Gold Coast, Sunshine Coast North, Sunshine Coast South and Rainbow Beach. Inside those four, both gears are set along the same stretch of coast, so the far north drops out of the comparison.
It is worth checking the question against the self-evident test, because at first glance it seems to fail it. A net takes whatever swims into it, so of course it catches the larger share of white sharks. Across the whole state it does: 1.8% of the nets' sharks against 0.9% of the drumlines'. In the four areas the order reverses: 3.8% on drumlines, 2.2% in nets. You cannot know which way it goes until you have added the numbers up.
Compare groups your strategy decides, and compare them only where both exist. The programme chooses the gear, which is what makes gear worth comparing; keeping to the areas that set both keeps place out of it. Fix a restriction like that before you look at the result, or it becomes a choice made to get one.
A hypothesis, and the temptation that comes with one
Optional and not assessed. If it makes the relationship you expect clearer, include it. If it is only decoration, leave it out.
This route comes with a temptation. Your results already exist. You can plot the thing in ninety seconds, see which way it goes, and then write a hypothesis that predicts it. Nobody can tell from the finished report, but it is not a hypothesis. A prediction made after the result predicts nothing. It is worth no marks, because the thinking it was supposed to show never happened.
So if you want one, write it before you plot anything, and keep it even when the data disagrees: a refuted hypothesis is a perfectly good result.
This is the small version of the integrity question that runs through the whole route. Writing a hypothesis after looking at the results does little harm. Choosing which years or which places to keep after looking does real harm, and step 4 deals with it properly.
Both have the same fix and it costs nothing: write the thing down first, in a file, with the date on it.
The one written before anything was calculated: nets will catch the larger share of white sharks, because a net takes whatever swims into it, while a baited hook takes what comes to the bait.
In the four areas it was refuted: white sharks were 3.8% of the drumlines' sharks and 2.2% of the nets'. Across the whole state it would have seemed to be supported, 1.8% against 0.9%, because the drumlines in the far north are set where white sharks do not swim. So the prediction failed in a way worth a paragraph of discussion, and the statewide figure shows how easily it could have looked right.
If it had been written after the calculation, it would have backed the drumlines. It would have been correct, and told you nothing.
Derive your hypothesis from how the system works, before you look, so that being wrong is informative. A prediction taken from a mechanism can fail in a way a reader learns from. A prediction taken from your own results cannot fail at all.
It can argue with a question you have already written: is the indicator named, are the units there, could the file you have actually answer it?
It cannot write it for you, and cannot tell you what your dataset contains. It will describe columns your file does not have, confidently. The sentence has to be checked against the file, not against a description of the file.
Ready for step 3?
Secondary data checklist0 of 19Read your sentence against every one of these, then read it against your open file. Fifty words can afford that many checks.
Your question is deliberately silent about people. Step 3 is where they arrive: somebody has a plan for the place your data describes, and not everybody agrees with it.
The white shark investigation used on the secondary-data route of this guide is the author’s own analysis of a published dataset: the Queensland Shark Control Program’s record of every animal caught on its nets and drumlines, published by the Queensland Government under CC BY 4.0 and downloaded on 26 September 2026. The choice of the four areas, the analysis and the conclusions are the author’s, not the Queensland Government’s.