Question bank › GCSE › GCSE Geography › Paper 3 – Geographical applications and skills

Geographical skills & fieldwork (incl. calculations)

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Answer on paper first, then reveal the mark scheme one mark at a time.

4 marks · 5 minutes

4 marks – unfamiliar fieldwork: do the data support the hypothesis?

A group of students investigated the hypothesis: 'The number of pedestrians decreases with distance from the town centre.' They counted pedestrians for 5 minutes at eight sites along one main road.

Figure 1: Pedestrian counts along a transect from the centre of a market town (5-minute counts, Saturday 10:00–11:30)
SiteDistance from town centre (m)Pedestrians counted
10142
2100118
320096
4300101
540064
650047
760052
870023

Using Figure 1, assess the extent to which the data support the students' hypothesis. [4 marks]

What is being assessed
  • AO3: interpret and evaluate data to reach a judgement
  • AO4: use quantitative skills (percentage change, mean, identifying anomalies)
What examiners reward
  • An overall trend described with data quoted from the figure
  • Manipulated data (e.g. percentage change, comparing means) rather than just copying numbers
  • Anomalies identified with figures
  • A clear judgement on 'to what extent'
LevelMarksWhat it looks like
Level 2 (Clear)3–4Clear interpretation of the data, using figures (ideally manipulated) to show both the overall trend and where the data do not fit, leading to a clear judgement on how far the hypothesis is supported.
Level 1 (Basic)1–2Basic interpretation, e.g. a general statement of the trend with little or no use of figures or recognition of anomalies; judgement absent or asserted.
How to plan it
  1. State the overall trend with first and last values
  2. Manipulate: % decrease from site 1 to site 8, or compare means of the inner and outer sites
  3. Identify anomalies: site 4 (101) and site 7 (52)
  4. Judgement
Show the top-band model answer

The data largely support the hypothesis. Pedestrian numbers fall from 142 at the town centre (0 m) to 23 at 700 m, a decrease of about 84%1. The mean count for the four sites closest to the centre (0–300 m) is about 114, compared with 46.5 for the four furthest sites, so the inner sites had more than twice as many pedestrians2 on average.

However, the decrease is not consistent: the count rises at site 4 (300 m) to 1013 from 96 at site 3, and at site 7 (600 m) to 52 from 47 at site 6. Overall, the data support the hypothesis to a large extent4, but these two anomalies mean the pattern is not perfect.

1. Data manipulated (percentage change), not just copied – rewarded under AO4.
2. Comparing means of groups of sites is a strong way to show the trend despite anomalies.
3. Anomaly identified precisely with figures.
4. Clear judgement, qualified by the evidence.
Where marks are usually lost
  • Copying every number from the table without describing the trend
  • Saying the data 'prove' the hypothesis
  • Ignoring the anomalies, or mentioning them without figures
  • Calculation slips – always state what the percentage is of (here, the decrease relative to the starting value of 142)
  • Writing about how to improve the method, which this question does not ask for – save it for questions on reliability
  • Forgetting to give a judgement
9 marks · 12–13 minutes (including SPaG check)

9 + 3 marks – own fieldwork: how far did your results support your hypothesis?

This question is about your physical geography fieldwork enquiry.

To what extent did the results of your enquiry support your original hypothesis or question? Refer to your data in your answer. [9 marks] [+3 SPaG marks]

The model answer below is based on an example river enquiry; your own answer must use the data you collected.

What is being assessed
  • AO3: evaluate your results and conclusions, and judge how reliable they are
  • AO4: use and manipulate your own data accurately
What examiners reward
  • Stating the hypothesis and the theory behind it (e.g. the Bradshaw model)
  • Using your actual data, with calculations such as percentage change
  • Identifying where results did NOT fit, with reasons
  • Evaluating how reliable the data were (sampling, equipment, conditions)
  • A balanced, justified conclusion on 'to what extent'
LevelMarksWhat it looks like
Level 3 (Detailed)7–9Detailed, accurate use of the student's own data (including manipulated figures) to show where results do and do not support the hypothesis. Thorough evaluation of how reliable the data and conclusions are, leading to a clear, justified judgement on 'to what extent'.
Level 2 (Clear)4–6Clear use of own results with some specific data. Some evaluation of where results fit or not, or of reliability, but one aspect may be thin and the judgement only partly supported.
Level 1 (Basic)1–3Basic or general reference to results with little or no actual data. Points are mostly descriptive (often of the method), with little evaluation and no clear judgement.
SPaG0–33 = spelling, punctuation and grammar consistently accurate, with precise subject terms used throughout; 2 = generally accurate with a good range of subject terms; 1 = reasonable accuracy with some subject terms; 0 = errors seriously hinder meaning or nothing relevant written.
How to plan it
  1. State hypothesis + link to the Bradshaw model
  2. Evidence for: width, depth, velocity, discharge all increase overall – quote % changes
  3. Evidence against: site 3 anomaly – explain
  4. Reliability: number of sites, float method, single day, weather
  5. Judgement: largely supported, with limits on confidence
Show the top-band model answer

Our hypothesis was that channel width, depth and velocity increase with distance downstream, as predicted by the Bradshaw model1, because tributaries add more water and the channel becomes more efficient. We measured five sites along a small stream on one day in June.

Results from five sites, measured from upstream (site 1) to downstream (site 5)
SiteWidth (m)Mean depth (m)Mean velocity (m/s)Cross-sectional area (m²)Discharge (m³/s)
11.20.110.210.130.028
22.10.180.340.380.129
32.00.240.290.480.139
43.40.270.420.920.386
54.60.350.471.610.757

Our results largely supported the hypothesis. Width increased from 1.2 m at site 1 to 4.6 m at site 5, an increase of about 283%2. Mean depth rose from 0.11 m to 0.35 m, about 218%, and velocity increased from 0.21 m/s to 0.47 m/s, about 124%. As a result, discharge increased from 0.028 m³/s to 0.757 m³/s3, about 27 times larger. Depth increased at every site, so this part of the hypothesis was fully supported4. This fits the theory: tributaries joined between our sites, adding water, and the larger, deeper channel downstream has less friction relative to its volume, so water flows faster.

However, the results were not consistent at every site. At site 3, width fell slightly5 from 2.1 m to 2.0 m and velocity fell by about 15%, from 0.34 m/s to 0.29 m/s. This may be because site 3 was on a bend where a fallen branch and thick vegetation slowed the flow, and the bank was harder to measure accurately. So width and velocity only increased overall, not at every site.

The reliability of our conclusion is limited. We only sampled five sites6, so one unusual site had a big effect on the pattern. Velocity was measured by timing a float over 10 metres; although we repeated each measurement three times and took a mean, the float was sometimes caught in eddies or blown by the wind. We also collected data on a single day after a dry spell, so the results may not represent the river at higher flows.

Overall, our results supported the hypothesis to a large extent7, as all three variables and discharge increased clearly from site 1 to site 5. However, the anomaly at site 3 and the small number of sites mean we can only be partly confident, and more sites sampled at regular intervals on several dates would make the conclusion more reliable.

1. Links the hypothesis to geographical theory – shows understanding of why the enquiry was designed.
2. Own data manipulated, not just quoted – credited under AO4.
3. Uses a derived value (area × velocity) to strengthen the evidence.
4. Precise: separates the variable that fully fits from those that only fit overall.
5. Identifies exactly where results did not fit, with figures.
6. Evaluation of reliability linked to the sampling strategy.
7. Judgement that is qualified by the reliability discussion.
Where marks are usually lost
  • Writing about the method in detail instead of the results
  • No real data quoted – 'it got bigger downstream' earns little
  • Ignoring results that do not fit the hypothesis
  • Confusing reliability (would we get the same results again?) with accuracy (how close to the true value?)
  • Answering about the human enquiry when the question asks about the physical one

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