Evaluation
A conclusion, and the discussion of bias, reliability, validity and uncertainty you wrote in step 6. Most of the raw material for this section is already on the page. What changes is what you do with it.
- Specific weaknesses, ranked by how much they mattered
- An improvement for each, with what it would fix
- Questions your investigation opened but could not close
- A finished report, bar the writing up
Six marks for saying what was wrong with your own work.
The same as your analysis is worth, and more than your strategy and method combined. Students tend to treat this section as an apology and hurry through it. It is the best return on effort in the entire report, and the only place where being hard on yourself is worth marks.
The bands turn on whether your weaknesses are generic or specific. The guide even defines generic for you: general to many methodologies, and not specifically relevant to the one you used. Which means the fix is mechanical. Take each sentence and ask whether it could have been written by someone who had never seen your investigation.
Human error may have affected the results.
Percentage cover was estimated by eye by one observer. Any consistent tendency to over-read moss would shift Simpson's index the same way at both sites, so the comparison survives it better than the absolute values do.
More data would have improved reliability.
Fifteen quadrats per site clears the threshold for a t-test but not the thirty wanted for a standard error of the mean. The difference between the sites is therefore better supported than the precision of either site mean.
The weather could have affected the results.
Sampling happened on one day in spring, but the mechanism under investigation operates in winter, through snow compaction. The vegetation was measured months after the disturbance it responds to.
Everything below is how we suggest you actually do it.
Three strands, one chain
The commonest structural mistake is to write three separate lists: weaknesses here, improvements there, questions at the end, none of them speaking to each other. The descriptors join them up explicitly. Improvements must address the limitations you identified. Questions must bear on your conclusion.
Write it as a chain and the marks follow the structure. Take your two or three most significant weaknesses and give each one a full run: what it was, what it did to your answer, what you would change, and what that change would buy you.
Specific to your method. And what it did to your conclusion, not just that it existed.
Addressing that named weakness. Realistic for a student with a school's equipment.
A question with a different focus from your original, and a bearing on your answer.
Quantify it if you possibly can
Most evaluations assert that a limitation was serious. Very few test it. If you can take your own data and calculate what would have happened under a different assumption, you have moved from claiming a weakness matters to showing whether it does.
This is what "evaluates" means as opposed to "describes", and it is worth more than another paragraph of careful prose. It is also, usefully, something a spreadsheet can do in ten minutes.
Mosses were recorded as a single group. Off-piste that group is 52% of all cover, which looks like it could be doing serious damage to a diversity index built on evenness.
Splitting that block into two plausible species and recalculating raises off-piste diversity from 2.18 to 3.24, which overturns the headline result. Alarming, until you notice the piste's feather moss is equally a lumped group at 45%. Split both, as consistent identification would, and the conclusion holds in every scenario tested.
So the real limitation is not lumping. It is uneven lumping between sites, and on that measure the two sites are comparable. The conclusion stands, with a caveat that can now be stated precisely rather than vaguely.
Improvements, evaluated
An improvement that is only stated earns the bottom band. Say what it would achieve and you are in the top one. Two extra clauses per improvement is the whole difference.
Keep them feasible. A second observer, a hand lens, an extra site, a return visit in a different season: these are things a student can actually do. Satellite imagery and a three-year monitoring programme are not improvements, they are daydreams, and examiners read them as such.
| Improvement | What it would buy |
|---|---|
| Two observers estimating cover independently | Turns an assumed bias into a measured one |
| A hand lens, mosses split to species | Tests the headline result rather than caveating it |
| A second piste and forest pair elsewhere | Separates management from this particular hillside |
Questions you opened but could not close
These have to point somewhere new. "What if I collected more data?" is not an unresolved question, it is an improvement wearing a disguise, and the guidance names it as exactly that.
A real one has a different focus from your original question, arises from something you actually found, and bears on how far your conclusion reaches. The best ones point back at your environmental issue, which is also the last opportunity in the report for the two halves of your work to touch.
Is the evenness advantage on the piste permanent, or a stage that an ungroomed slope passes through on its way back to forest? The strategy assumes a destination; this study only saw one moment.
Would summer grazing alone hold the sward open without winter grooming? The two are confounded here, and separating them would tell the graziers and the operator whether their interests actually align.
Ready for step 8?
0 of 10Eight of these are the criterion itself. It is the most prescriptive one in the rubric, which makes it the easiest to score well on.
Every section now exists in some form. Step 8 is assembly, referencing and the word count, and it is where the budgets you have been carrying since step 1 finally get added up. Aim for 2,900, not 3,000, and you will find out why.
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.