Conclusions and Evaluation in Fieldwork Revision Notes | GCSE AQA Higher Geography

1. Why Conclusions and Evaluation Are the Heart of Paper 3

You have collected your data, drawn your graphs, and described your results. Now comes the part that separates a Level 2 answer from a Level 3 one: reaching a conclusion and evaluating your enquiry honestly. These two skills are tested directly in Paper 3, and they carry significant marks.

Think of it this way. A scientist who runs an experiment but never says what it proves has wasted their time. Your conclusion is your answer to the original enquiry question — and your evaluation is your honest verdict on how much you can trust that answer.

The two skills are linked. A strong conclusion says what the data shows. A strong evaluation says how far you can believe it. Together, they demonstrate the kind of geographical thinking that AQA rewards at the top of the mark scheme.

2. Reaching a Conclusion: Answering the Enquiry Question

A conclusion is not a summary of what you did. It is a direct answer to the question your enquiry set out to investigate. If your enquiry question was "Does pedestrian footfall decrease with distance from the town centre?", your conclusion must state whether it does, does not, or partially does — and back that up with your data.

The strongest conclusions do three things: they state a clear judgement, they reference specific data as evidence, and they link back to geographical theory or concepts. For example, a conclusion about river velocity should mention the Bradshaw Model if the data supports it — that connection to wider geography is what lifts an answer into the top band.

Notice the difference between these two conclusions. Weak: "The results show that pedestrian numbers were higher near the shops." Strong: "Pedestrian footfall decreased with distance from the town centre, falling from 142 people per minute at Site 1 to 23 at Site 5, which supports the hypothesis and is consistent with the concept of retail gravity, where footfall concentrates around the highest-order services."

Worked example: How to Write a Strong Fieldwork Conclusion

Your enquiry question is: 'Does river velocity increase downstream?' Your data shows mean velocity of 0.12 m/s at Site 1, 0.31 m/s at Site 3, and 0.58 m/s at Site 5. Write a conclusion.

1. State whether the data supports, partially supports, or contradicts the hypothesis. — This is your opening judgement. Do not hedge — commit to a position based on what the data shows.
2. River velocity increased from 0.12 m/s at Site 1 to 0.58 m/s at Site 5 — a rise of 0.46 m/s across the five sites. — Specific figures are essential. Vague references to 'higher values' do not secure marks. Quote the actual numbers.
3. Velocity increased progressively at each downstream site, with the largest single increase between Sites 3 and 5. — Describing the pattern shows you can interpret data, not just read it off a graph.
4. This supports the Bradshaw Model, which predicts that velocity increases downstream as the channel becomes wider and smoother, reducing friction. — Connecting your findings to a named geographical concept or model is the mark-winning move at Level 3.
5. Site 4 showed a slight decrease in velocity (0.44 m/s), which may reflect a localised increase in channel roughness or a meander bend at that location. — Acknowledging anomalies shows analytical thinking. Ignoring them suggests you have not fully engaged with the data.

Answer: River velocity increased downstream, rising from 0.12 m/s at Site 1 to 0.58 m/s at Site 5, supporting the hypothesis and the Bradshaw Model. One anomaly at Site 4 may reflect local channel conditions.

3. Evaluating Your Geographical Enquiry

Now that you can write a conclusion, the next challenge is evaluating how much you can trust it. Evaluation is not about saying everything went wrong — it is about making an honest, balanced judgement of the enquiry's strengths and weaknesses, and suggesting specific improvements.

AQA expects you to evaluate four interconnected aspects of your enquiry: the data collection method, the sampling strategy, the reliability of the results, and the validity of the conclusions. Each of these connects back to the method-to-results chain you followed during the fieldwork itself.

Notice that each evaluation point follows a chain: identify the issue, explain why it matters, and suggest a specific improvement. Vague statements like "the results might not be accurate" earn no marks. Specific, reasoned points do.

3a. Evaluating Sampling: The Method-to-Reliability Chain

Your sampling strategy directly affects how reliable and representative your results are. When evaluating sampling, you need to consider three things: the type of sampling used, the sample size, and whether the sample was representative of the wider population or area being studied.

• Random sampling: removes personal bias in site selection, but may miss important areas by chance — a larger sample reduces this risk.
• Systematic sampling: ensures even coverage across a transect or area, but may miss variation between sample points.
• Stratified sampling: ensures subgroups (e.g. different land uses or river sections) are proportionally represented, making results more valid for comparison.
• Sample size: a larger sample increases reliability because it reduces the effect of anomalous results on the overall pattern.

A common evaluation trap is to say "we should have used a bigger sample" without explaining why. The stronger version is: "Our sample of five river sites may not capture all variation in channel characteristics; increasing to ten sites, including sites in both straight and meandering sections, would improve the representativeness of the results."

3b. Reliability and Validity: Two Different Things

Reliability and validity are often confused, but they test different things. Reliability asks: if we repeated this, would we get the same result? Validity asks: are we actually measuring the right thing in the right way?

Here is a concrete example. Suppose you measure noise levels in a town centre using a decibel metre at 10 am on a Tuesday. The method is valid — a decibel metre does measure noise. But the results may not be reliable or representative, because noise levels at 10 am on a Tuesday may differ significantly from a Saturday afternoon. Repeating the measurement at different times and days would increase reliability.

Validity problems are different. If you tried to measure the quality of a river environment using only water temperature, that would be a validity issue — temperature alone does not capture the full picture of river quality. A more valid approach would include pH, turbidity, and invertebrate counts alongside temperature.

4. Interpreting and Analysing Evidence: Applying Your Skills

Reaching a conclusion and evaluating your enquiry both require you to interpret and analyse the evidence you collected. This means going beyond describing what the data shows and explaining what it means in a geographical context.

When you analyse evidence, you are looking for patterns, trends, anomalies, and relationships. A pattern is a consistent change across your data (for example, increasing pebble roundness downstream). An anomaly is a result that does not fit the pattern and needs explaining. Identifying and explaining anomalies is a mark-winning skill that many students overlook.

Selecting the right evidence to support your conclusion also matters. If your enquiry produced both quantitative data (counts, measurements) and qualitative data (photographs, field sketches, questionnaire responses), you should draw on both types where they strengthen your argument. Quantitative data gives precision; qualitative data gives context.

5. Exam Technique: Writing for Marks in Paper 3

Paper 3 fieldwork questions use specific command words that tell you exactly what kind of response is needed. Getting the command word right is the first step to accessing the top mark band. The most common command words for conclusions and evaluation are 'explain', 'assess', 'evaluate', and 'to what extent'.

When a question asks you to 'evaluate' your fieldwork enquiry, it is asking for a balanced judgement — strengths and weaknesses — with a conclusion about overall quality. When it asks you to 'assess' the extent to which your data supports a conclusion, it wants you to weigh the evidence and reach a clear verdict, not sit on the fence.

Worked example: How to Answer a Fieldwork Evaluation Question

Evaluate the extent to which the data collected during your fieldwork supports the conclusion reached. [6 marks]

1. Open with a clear verdict: 'The data largely supports the conclusion, although some limitations reduce confidence in the findings.' — Examiners want to see a judgement, not a description. State your position in the first sentence so the rest of your answer builds on it.
2. The systematic sampling strategy ensured even coverage across the study area, and the consistent increase in velocity across all five sites (0.12 to 0.58 m/s) provides strong evidence for the trend. — A strength must be specific — name the method, explain why it works, and link it to the data pattern it produced.
3. However, measurements were taken on a single day in dry weather, so results may not reflect typical conditions. Seasonal variation in discharge could alter velocity patterns significantly. — A limitation must explain the impact on the results, not just name the problem. 'We only went once' earns no marks; explaining why that affects reliability does.
4. Repeating measurements across three different seasons and increasing the number of sites from five to ten would improve both reliability and representativeness. — Improvements must be specific and linked to the limitation identified. Vague suggestions like 'do more research' do not demonstrate geographical understanding.
5. Overall, the data provides reasonable support for the conclusion, but the limited sample size and single-day collection mean the findings should be treated as indicative rather than definitive. — Ending with a clear, qualified judgement is what pushes an answer into the top band. 'To a large extent' or 'partially' are both acceptable — what matters is that the judgement is justified.

Answer: The data largely supports the conclusion, with a consistent downstream increase in velocity across all five sites. However, single-day collection and a small sample size limit reliability. Repeating measurements across seasons and increasing site numbers would strengthen confidence in the findings.

One final point on exam language. When a question asks 'to what extent does your data support your conclusion?', you must take a position. Answers that say 'the data both supports and does not support the conclusion' without reaching a verdict cannot access the top band. Commit to a qualified judgement — 'to a large extent', 'partially', or 'to a limited extent' — and justify it with evidence.