Geographical enquiry

Use a fictional investigation to connect variables, sampling, representation and evidence-based evaluation.

Two adults use fieldwork instruments on a rocky shore.

Part 1 of 4

Identify what a question measures

A geographical enquiry needs a measurable relationship or comparison and a defined area and period. Distinguish explanatory variables from outcomes and identify other influences.

Fictional training scenarios teach method choices. They do not design an assessed investigation for a particular student.

Worked example

A fictional class asks whether pedestrian counts differ by distance from a transport stop. Distance is the explanatory variable; a count during a fixed ten-minute interval is the outcome.

Common mistake: Changing count duration between sites changes the comparison.

Sampling methods · Mean, median and range

Next: Choose and describe a sample →

Part 2 of 4

Choose and describe a sample

Random selection gives defined units a known chance of inclusion. Systematic selection follows a stated interval. Stratification separates relevant groups before sampling.

A convenient sample can exclude parts of the target population. Repeating the same biased procedure improves precision around an unrepresentative result.

Worked example

A fictional population has 60% central and 40% outer sites. A proportional stratified sample of 20 contains 12 central and 8 outer sites, with selection still specified inside each group.

Common mistake: Stratification does not fix biased selection within groups.

Sampling methods · Percentage change

Next: Build a conclusion with limits →

Part 3 of 4

Build a conclusion with limits

Choose graphs that suit variables and the comparison. Scatter plots describe paired numerical association; category comparisons need an appropriate representation.

State what the observed pattern supports, how reliable it is and what remains unresolved. Evaluation connects a limitation to its likely effect and a feasible general improvement.

Worked example

Fictional counts of 10, 12, 13 and 45 have mean 20 and median 12.5. The high count pulls the mean upward; investigate whether it reflects a real event or an error.

Common mistake: Removing an awkward observation without justification distorts evidence.

Mean, median and range · Scatter plots · Source evaluation

Next: Triangulation and conflicting evidence →

Part 4 of 4

Triangulation and conflicting evidence

Two sources can disagree because they measure different things. A resident’s account of an unsafe street and a low recorded collision rate concern related but distinct outcomes. Reporting, exposure and time of day may explain part of the difference.

Triangulation compares methods to investigate the disagreement. It does not turn an unrepresentative survey into a representative one by adding photographs. Explain what each method can establish, whose experience is included and where your conclusion remains uncertain.

Worked example

A fictional survey has 18 of 30 respondents reporting difficulty crossing a road: 60%. An observation records 12 delayed crossings among 80 pedestrians: 15%. These percentages have different denominators and definitions, so subtracting them does not measure a contradiction.

Common mistake: Different percentages cannot be compared meaningfully without checking what was counted.

Choosing a data representation · Source evaluation

Try a fresh question →

Try a different resource

Choosing a data representation

The task is to inspect association between two paired quantitative variables. Several displays can be useful for other purposes; choose the one matching this specific aim.

Site	Distance (km)	Traffic count
A	1	70
B	2	60
C	3	64
D	4	45

Fictional representation exercise All values are synthetic.

Resource description

Fictional representation exercise

Write an explanation

Evaluate the strength of a conclusion from a geographical enquiry.

Check your answer

Link question, sample and representation.
Explain how a limitation affects the conclusion.
Distinguish association from causation.
Use generic training only; follow your centre’s assessed-work rules.

Practise and review a written answer

Case studies

Carlisle during Storm Desmond · Bristol’s clean air zone

These pages provide selected evidence. Check the whole case requirement with your teacher.

Read further

Sampling and representativeness · What a statistical test can establish · Association and geographical explanations · Matching evidence to a claim

Course links

AQA GCSE Geography · Pearson Edexcel GCSE Geography B · National 5 Geography · Higher Geography · Pearson Edexcel GCSE Geography A · OCR GCSE Geography A · OCR GCSE Geography B · Eduqas GCSE Geography A · Eduqas GCSE Geography B · OCR A-level Geography · Eduqas A-level Geography

Next topics

Changing places: meaning, evidence and connections · Hazard systems: cascades, probability and resilience

Selected teaching sequence with fictional worked data. The linked case records supply bounded evidence; they do not satisfy every course case requirement.

Scottish links identify reusable parts of the topic. Scottish landforms, soil, weather and disease requirements need additional course-specific teaching.

Worked numerical examples are fictional unless explicitly attributed. These lessons cover selected parts of the topic; check your course requirements for the rest.