Collect and treat your data
A notebook or spreadsheet of raw readings, and ideally a decision already made about which statistical test you are going to run. If you have not chosen one yet, do it before you start processing.
- Raw data, tabulated
- Processed data that shows the pattern
- A statistical result, correctly calculated
- No interpretation whatsoever. That is next.
Six marks, and almost none of the evidence costs you a word.
This is the best-value section in the whole report, and most students never notice. Everything that earns marks here sits outside the 3,000: the tables, the graphs, the calculations. What costs words is describing them, and describing them earns nothing.
Data tables, raw and processed. Every graph and chart. Equations, formulae and calculations. Diagrams and photographs. Citations and the bibliography.
Spend freely. Make them excellent.
The sentence saying what each table shows. Why you chose that statistical test. Your null and alternative hypotheses. What you did about outliers, and why.
About 200 words in total. That is all you need.
One caution that catches people out: appendices are not read by the examiner. Anything that matters has to be inside the report. Since tables do not count against you wherever they sit, put them in the body where they will actually be seen.
Everything below is how we suggest you actually do it.
Raw, then processed, then stop
Raw data goes in tables. Not lists, not prose, not a photograph of your field notebook. Separate tables for raw and processed are perfectly acceptable and usually clearer.
Then process it into something that shows the pattern. A well-made graph of raw data is not processing, and it is not analysis either. Something has to be calculated: an index, a mean with its spread, a correlation.
You do not need to show a worked example for standard processing. Nobody needs to see how you calculated a mean. Show the working where the calculation is unusual or where a reader would otherwise have to guess.
Percentage cover per species per quadrat is the raw data: thirty rows of it. Simpson's diversity index calculated per quadrat is the processed data, and one worked example is shown because the formula is not something a reader can assume.
The means, standard deviations and t-tests come next. All of it in tables, none of it costing a word.
Choosing the graph and the test
The graph type is a real decision, not a default. Comparing two groups wants a bar chart with error bars. Looking for a relationship between two continuous variables wants a scatter plot with a line of best fit. Picking the wrong one hides the very pattern you are trying to show.
Where you can, run an inferential test and report it properly: your null and alternative hypotheses, the statistic, the degrees of freedom, and the probability.
Error bars are worth understanding rather than decorating with. If they do not overlap, the means differ significantly. If they do, they do not.
Say which one you used and why. "A t-test was used because the two sites were independent groups" is one sentence and it earns its place.
Outliers, and the small things that cost marks
Systematically deleting awkward points is not acceptable. If a value has to go, it needs a full justification, and "it looked wrong" is not one. In almost every case the better move is to keep it and explain it, because an explained outlier is evidence of thinking and a deleted one is evidence of nothing.
Then the quiet mark-losers: a table without a title, an axis without units, decimal places implying precision your instrument never had. None of these are hard. All of them are noticed.
One forest quadrat scored 4.25 for diversity when the others sat between 1.5 and 2.6. Tempting to call it an error and drop it.
It turned out to be a gap in the canopy, and once canopy cover was measured it stopped being an anomaly and became the clearest single piece of evidence for what was driving the whole pattern. Deleting it would have thrown away the best result in the study.
Ready for step 6?
0 of 9The last one is the hardest to obey and the most valuable.
Everything you have just been told not to do is what step 6 is for. The patterns you can see in your graphs, what is causing them, and what it means for your research question: all of that goes there, where six marks are waiting for it.
The ski piste investigation used throughout this guide is a teaching reconstruction. The location, sampling design, plant records and soil data come from real fieldwork at Mijoux in the French Jura. Canopy cover measurements were added to illustrate good practice and were not part of the original fieldwork.