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Drawing conclusions is one of the most important stages of any geographical investigation. It is where you bring together all your data, analysis and geographical knowledge to answer the enquiry question you set at the beginning. A strong conclusion does not simply repeat your findings — it interprets them, relates them back to your hypothesis, and considers the wider geographical significance of what you have discovered.
In the Edexcel B exam, conclusion questions can carry up to 6–8 marks. Examiners want to see that you can move beyond description to make evidence-based judgements about your investigation.
Every conclusion must start by returning to the original hypothesis or enquiry question. This provides the framework for your conclusion and ensures you stay focused.
| Outcome | What It Means | Example |
|---|---|---|
| Hypothesis supported | The data provides evidence that supports the prediction | "The data supports the hypothesis that pebble size decreases downstream, with a Spearman's rank coefficient of rs = -0.95 (significant at 0.05 level)" |
| Hypothesis partially supported | Some data supports the prediction, but not all | "The hypothesis is partially supported: pebble size generally decreases downstream, but Site D is an anomaly where pebble size is larger than expected due to tributary input" |
| Hypothesis rejected | The data does not support the prediction | "The data does not support the hypothesis. There is no significant correlation between distance from the CBD and environmental quality scores (rs = 0.15, not significant)" |
Exam Tip: It is perfectly acceptable to conclude that your hypothesis was wrong. Examiners are not looking for a "correct" answer — they want to see that you can honestly interpret your data and explain why the results might differ from your prediction. Rejecting a hypothesis is just as valid as supporting it, as long as you explain your reasoning.
A strong conclusion follows a logical structure. Use this framework:
Begin by reminding the reader of what you were testing:
"The aim of this investigation was to test the hypothesis that mean pebble size decreases with distance downstream along the River Exe, due to the processes of attrition and abrasion."
Give a clear, direct answer to the enquiry question:
"Overall, the data strongly supports this hypothesis. There is a clear negative relationship between distance downstream and pebble size."
Back up your finding with specific data:
"Mean pebble size decreased from 85 mm at Site A (0.5 km from the source) to 18 mm at Site H (4.0 km from the source). The Spearman's rank correlation coefficient was rs = -0.95, which is significant at the 0.05 level (critical value for n = 8 is 0.738). This means we can reject the null hypothesis with 95% confidence."
Link your findings to theory:
"This pattern is consistent with the Bradshaw model, which predicts that sediment size decreases downstream. The dominant processes are attrition (pebbles collide and fragment, producing smaller pieces) and abrasion (pebbles are worn smooth by contact with the riverbed and each other). In the upper course, where gradient is steepest and energy is highest, the rate of size reduction is greatest."
Address any data that does not fit the pattern:
"Site D had a larger mean pebble size (45 mm) than the downward trend would predict. This anomaly may be caused by a tributary joining the river upstream of Site D, introducing coarser sediment from its own catchment area. This demonstrates that local factors can disrupt the general downstream trend."
Place your findings in a broader context:
"These findings confirm that the Bradshaw model is a useful tool for predicting downstream changes in river characteristics, though local factors such as tributary inputs and human modifications (e.g. weirs) can cause deviations from the expected pattern."
flowchart TD
A["RESTATE HYPOTHESIS<br/>'Pebble size decreases downstream'"] --> B["STATE FINDING<br/>'Data strongly supports hypothesis'"]
B --> C["PROVIDE EVIDENCE<br/>'rs = -0.95, significant at 0.05'"]
C --> D["EXPLAIN WITH THEORY<br/>'Bradshaw model, attrition, abrasion'"]
D --> E["ADDRESS ANOMALIES<br/>'Site D — tributary input'"]
E --> F["WIDER SIGNIFICANCE<br/>'Bradshaw model confirmed with caveats'"]
When drawing conclusions, you must consider how much you can trust your findings:
Reliability refers to whether the results would be the same if the investigation were repeated.
| Factor Affecting Reliability | Example | Impact |
|---|---|---|
| Sample size | Only 5 pebbles measured per site | Low reliability — too few measurements to be confident |
| Repeated measurements | Velocity measured 3 times and averaged | Higher reliability — reduces the effect of individual errors |
| Standardised methods | Same person measured all pebbles using the same ruler | Higher reliability — removes inter-observer variation |
| Consistent conditions | All measurements taken on the same day | Higher reliability — removes temporal variation |
| Equipment accuracy | Digital flow meter vs float method | Flow meter is more reliable — more precise measurement |
Validity refers to whether the investigation actually measures what it claims to measure.
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