Conclusions, Evaluation & UK Decisions
Closing the Enquiry Loop
The last two stages of the enquiry process are drawing conclusions and evaluating the work. A conclusion answers the original question using evidence from your data. An evaluation is a critical look back at how you collected and analysed the data: what worked, what did not, and what you would change. Both are examined in this paper, so this is where strong students separate from weak ones.
A separate part of the fieldwork paper also asks you to step beyond your fieldwork and apply geographical thinking to a UK decision-making task. Same skills — evidence, evaluation, judgement — new context.
Stage 5 — Drawing Evidenced Conclusions
A conclusion in fieldwork is a short answer to the original question, supported by figures from your own results. It refers back to the hypothesis, says whether the data supports or rejects it, and links the result to a wider geographical idea.
A strong conclusion follows the pattern: state whether the hypothesis is supported → quote one or two specific figures → link the result to a geographical process or theory. Vague conclusions like ‘the data supports the hypothesis’ lose marks.
For example, a Welsh river study might conclude: ‘Pebble long-axis decreased from a mean of 64 mm at site 1 to 21 mm at site 5, supporting the hypothesis. The pattern is consistent with attrition during transport, where pebbles wear smaller as they are carried downstream.’ The figures and the theory link are what lift this above a description.
Sometimes the data does not support the hypothesis, and that is fine. The specification stresses that ‘geography can be messy’. Honest conclusions that admit unexpected results — and try to explain them — score higher than ones that twist the evidence to fit the prediction.
Stage 6 — Evaluating the Enquiry
Evaluation has three main targets: methods, data and conclusions. For each, judge what was strong, what was weak, and how it could be improved.
Ask whether your sampling method gave a representative picture. Was random sampling really random, or did you skip awkward sites? Was systematic sampling spaced widely enough to catch local variation? Was your sample size large enough? On a beach pebble study, 30 pebbles per site is a common minimum — fewer than that and a single oddly shaped pebble can swing the mean.
Equipment also matters. A clinometer reading is only as accurate as the person using it. Tape measures stretch and sag if they are not held taut. Questionnaires are biased if you only ask people on a sunny weekend. Pin down the source of any error rather than just saying ‘readings could have been more accurate’.
Reliability, Bias and Accuracy
Three key terms come up in evaluation questions. Accuracy is how close your measurement was to the real value. Reliability is whether the same method would give similar results if repeated. Bias is a systematic distortion — for example, only surveying shoppers on a Saturday morning gives a misleading picture of the high street as a whole.
Accuracy and reliability are not the same thing. A clinometer reading can be accurate but unreliable if only one person can use it correctly. Reliable methods are repeatable; accurate methods are correct. Both are needed for a strong enquiry.
Strengths and Limitations of Data
Evaluation also considers your data sources. Primary data is up-to-date and matched to your specific question, but is limited by the time and weather of the day you collected it. Secondary data covers longer periods and wider areas, but it was collected for someone else’s purpose — so it may not match your sites exactly. Strong evaluations weigh the two, noting that they complement each other.
Stakeholders matter too. The spec reminds us that primary or secondary sources may have vested interests: a tourism board reporting visitor satisfaction has reason to highlight the positive, an environmental group reporting reef damage has reason to highlight the negative. Treat sources critically rather than at face value.
Suggesting Improvements
A good evaluation always ends with realistic improvements. Vague suggestions like ‘take more readings’ gain little credit. Specific ones do: ‘Repeat the survey on a weekday and a weekend so weekly variation is captured.’ ‘Hold the tape taut between two fixed points so it cannot sag and stretch.’ ‘Include both Welsh and English coastal sites to test whether the pattern holds in contrasting environments.’
Applying Ideas to UK Decisions
The final part of the fieldwork paper sets a UK decision-making task. You are given a set of resources — maps, graphs, photographs, news extracts — about a real UK issue such as a coastal management scheme, a wind farm proposal, or a new flood defence. You must read the evidence, weigh the options, and recommend a course of action with reasons.
The skills are the same as for fieldwork: read the data carefully, identify patterns, weigh strengths against limitations, and reach a clear judgement supported by evidence. Strong answers state which option you would choose, give two or three reasons drawn from the resources, then briefly acknowledge the trade-offs of the option you rejected.
Imagine you are given two options for a Norfolk coastline: hold the line with rock armour, or managed retreat. Resources include cost estimates, photographs of cliff retreat, and population data for the village behind the cliffs. A strong answer might recommend managed retreat because the long-term cost is lower, the cliffs are eroding too fast for hard defences to work indefinitely, and a salt marsh would form behind the new shoreline. It would acknowledge that residents lose homes and need fair compensation — an honest trade-off rather than a one-sided recommendation.