Analysis and conclusion
Processed data, figures you built yourself, and a statistical result. No interpretation yet, because that is the six marks on this page.
- Every pattern described and then explained through the system
- Two or three ways your data source could mislead, each with a direction
- A conclusion in numbers that reaches exactly as far as your rows
- A return to the issue and the strategy
Six marks for saying what your numbers mean, and one extra job this route cannot skip.
The ladder is describe, explain, reconnect. What this route has to do as well is be honest about its instrument: you controlled nothing, you measured nothing, and the collection process that produced your values had its own purposes and its own failures. Naming those precisely is where most of the marks on this page actually are.
A strong analysis does three things, in order.
Name the pattern precisely. Which direction, how strong, and whether the statistics say it is real.
On its own, this is the lower band.
What process produced the pattern? This is where your abiotic measurements matter, because they are the mechanism: the process that causes it.
This is what moves you into a higher band.
Take the answer back to the environmental issue and the strategy you wrote about in step 3.
This is where the work stays ESS.
Everything below is how we suggest you actually do it.
Four things, one narrative
Bias, reliability, validity and uncertainty all have to be here. The common mistake is to give each one its own labelled paragraph, which makes them easy for an examiner to find and impossible to read.
Weave them instead. Take one trend, connect it to your question, then work through how far you trust it and why. It reads as thinking rather than as a checklist.
Two things you drew in step 5 must be spoken about here. Error bars: do they overlap, and what does that let you say? A line of best fit: how much of the variation does it account for? Drawn and never mentioned, they earn nothing.
In Biology, Chemistry and Physics the conclusion is expected to link back to published research. ESS does not require it.
You may still bring a source in, if it helps explain why your result came out the way it did. Showing that you read widely on the subject belongs in your background, where it is assessed.
Describe, explain, reconnect
Three steps, and on this route it is easy to get stuck on the first, because a downloaded series is so easy to describe.
Explaining means naming the process that produced the pattern, in the environmental system you drew in step 1. Not that the numbers fell, but what happened, to what, that made them fall. That is where your systems diagram stops being decoration.
Causation is the hard part here, and you should say so rather than hoping nobody notices. You controlled nothing physically. Everything you have is association, and the honest move is to name the mechanism you think is operating, then name the other things that could have produced the same pattern.
Describing: in the four areas that set both gears, 1996 to 2023, white sharks were 3.8% of the sharks caught on drumlines and 2.2% of those caught in nets (chi-squared 11.6, p about 0.0007). Drumlines had the larger share in every one of the four areas, from the Gold Coast (8.0% against 4.5%) to Rainbow Beach (1.5% against 0.8%).
Explaining, through the system: white sharks pass this coast in winter and spring, and 124 of the 147 were caught from June to October. The nets catch most of their other sharks in summer, when few white sharks are caught: only 30% of the nets' sharks came in June to October, against 43% of the drumlines'. From November to May the nets caught 2,294 sharks, and 10 of them were white sharks. That summer catch piles up at the bottom of the nets' fraction and dilutes their white share.
Then the check that turns a story into an explanation. Compare the gears only in the months white sharks pass, June to October, and the shares close up: 7.3% on drumlines, 6.3% in nets, chi-squared 0.8, p about 0.38, no significant difference. So most of the gap comes from when each gear catches its other sharks, not from which sharks it catches. The hypothesis, written before looking, was that a net takes whatever swims into it and so would take the larger share. In these four areas the data does not support it.
And one alternative that is not about gear at all. Across the whole state the answer reverses, nets 1.8% and drumlines 0.9%, and the hypothesis would have looked right. Drumlines are also set in the north, from Cairns to Bundaberg, where the sharks are almost all tiger sharks, whalers and hammerheads and there are no white sharks. In a statewide share, place and gear look identical, which is why the four areas were fixed before looking.
Write the mechanism as a chain of events with names on it, then immediately name what else could have produced the same numbers. On this route that second sentence is not weakness, it is the thing that separates analysis from a press release.
Five ways a dataset misleads you
Your "instrument" was somebody else's data collection, and these are the five ways it typically fails.
A catch is not a population, and a share of a catch is not a rate. GDP per capita is not environmental stewardship. Ask what your indicator actually is, and what you are quietly claiming it stands for.
A national figure can hide severe local damage; a site mean can hide the storm peaks that cause the problem. A pattern that is true of the total need not be true of any part of it.
A census every five years against monitoring every month. An annual mean against a strategy that started in June. If your two series are not on the same clock, saying so is part of the analysis.
Figures submitted by the party being judged by them. Under-reporting where reporting is costly, absence where monitoring is absent. Missing data is rarely missing at random.
Remote sensing reads plantations as forest. An automated category groups things you would separate. Every classified dataset has a confusion it is known for, and its documentation usually says so.
The test for whether you have done this properly: can you say which direction each one would push your result? "Reporting bias may exist" is the bottom band. "Countries with weaker monitoring report lower values, which would flatten the relationship I found rather than create it" is the top one, because it tells a reader what to do with your conclusion.
Temporal misalignment, which here is a confounding variable: the season. White sharks pass in winter and spring, and the nets catch most of their other sharks in summer. Pooled over the whole year, that pushes the nets' white share down and makes the gap look bigger than it is. It is the largest of the five: within June to October the difference is no longer significant.
Aggregation, by place. Take all of Queensland and the answer reverses, because drumlines are also set from Cairns to Bundaberg, where there are no white sharks. Including the north pushes the drumlines' share down. Keeping only the four areas that set both gears removes it, and that rule was fixed before looking.
Proxy validity: a share of the catch is not a rate. The file does not say how many nets and drumlines were in the water, or for how long, so it cannot say which gear catches more white sharks a day. Which way that pushes is unknown, and the honest sentence says so and says why.
Classification error: 18 sharks are recorded only as UNKNOWN SHARK and sit with the other sharks. Any of them could have been a white shark, which would push the true white shares slightly up. Which gear that would favour, the file cannot say.
Reporting bias: the programme records its own catch, and from 2024 it changed what it sets and how often it checks it. That is why the series stops at 2023.
The report spends its words on the first two, because they are the ones that change the answer.
Work through all five for your own file, then spend your words on the two or three that move your answer most, and for each one say which way. A limitation with a direction is analysis; a limitation without one is a disclaimer.
A conclusion that claims exactly what you found
Answer your research question, in numbers, in a couple of sentences. Nothing may appear here that was not in the analysis above it, and if you stated a hypothesis you must say whether the data supports it.
Then the discipline this route needs most: your conclusion reaches exactly as far as the rows you kept. Not the whole state, if you kept four areas. Not the present, if your series stops at 2023 because the programme changed after it. Not a rate, if all you have is a share.
Drumlines catch more white sharks than nets, so they are worse for a protected species.
Two claims, and the data supports neither: it measured a share of each gear's catch, not how many white sharks a gear catches for its time in the water, and most of the difference turned out to be the season.
Across 1996 to 2023, in the four areas that set both gears, white sharks made up a larger share of the drumlines' shark catch (3.8%) than of the nets' (2.2%), a difference unlikely to be chance (chi-squared 11.6, p about 0.0007). But in the months white sharks pass, June to October, the two shares were close (7.3% and 6.3%) and not significantly different. The difference comes mostly from when each gear catches its other sharks, not from which sharks it catches.
A reader knows exactly what has and has not been shown. The hypothesis, that nets would take the larger share, then gets a sentence of its own: not supported in these four areas.
"Drumlines catch more white sharks than nets." It is a share, not a rate. The file does not say how many nets and drumlines were in the water, or for how long, so it cannot say which gear catches more white sharks a day. In plain numbers the two caught 74 and 73.
"Drumlines are worse for white sharks." The season test says most of the difference is timing, and whether the sharks were alive when the gear was checked points the other way, if anything.
"Shark control makes beaches safer." Nothing here measures bites or swimmers.
"White sharks are declining." The catch depends on the gear and how much of it is in the water, not only on how many sharks there are.
Then travel back
The last thing your conclusion should do is return to where step 1 started: the environmental issue, and the strategy people are arguing about. This reconnection is what makes it an ESS investigation rather than a piece of data analysis. On this route it is the only place where the two halves of your report meet.
It does not need many words. What did your numbers add to the argument you described in step 3? Whose position do they support, whose do they complicate, and what would each side say about them?
Travel back along the three lines. Say what your result means for what people do (line 1), for what happens to nature (line 2), and for the plan (line 3). If the plan is itself line 1, as the gear is in the shark study, your result bears on it directly; if it only meets the IB minimum, say how far your result can reach it.
The tension in step 3 was swimmers' safety and a beach economy against wildlife. The Shark Management Plan 2025 to 2029 added lethal gear at seven new locations, with daily servicing, although the government's own KPMG review had advised moving away from nets and drumlines. The question asked what each of the programme's two gears does to one protected shark, which is the choice the plan makes.
What the numbers add is narrow. They do not show that drumlines are gentler on white sharks by share: across the year the drumlines' share was the larger, and in the months white sharks pass the two were close. So as far as white sharks go, a choice between the gears cannot rest on which one takes the smaller share.
They cannot touch the other half of the tension. Nothing in the file measures bites or swimmers, so it supports neither the plan's case for safety nor the scientists' view that culling does not make beaches safer.
And the file holds one thing the question did not ask, which bears directly on the argument: 37 of the 74 white sharks on drumlines and 28 of the 73 in nets were alive when the gear was checked. Caught is not killed. That difference was not tested, so it goes to step 7 as an unresolved question, not into the conclusion.
Say what your result does to each position you described in step 3, including the one you expected to lose, and name the part of the argument it cannot touch. A narrow finding stated precisely is usually the honest one, and it is far more interesting to read than a verdict.
It can do nothing here. If a general idea needs clarifying, do that before you open this section, not inside it.
It cannot interpret your results. Explaining your own pattern is what the six marks are for, and a tool that cannot see your file will produce a paragraph that sounds like analysis and contains none.
Ready for step 7?
Secondary data checklist0 of 17Four of these belong to this route, and the one asking which direction each weakness would push your result is what separates the top band from the middle.
You have just written about bias, reliability, validity and uncertainty. Step 7 takes the same material and does something different with it: not how far your data can be trusted, but what you would change and what it would fix. Most of that material is already written.
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.