Sampling Methods

When you carry out geographical fieldwork, it is almost never possible to measure everything. You cannot measure every single pebble on a beach, ask every person in a town centre, or record the depth of a river at every possible point along its course. Instead, you take a sample — a smaller, manageable selection of measurements that you hope will be representative of the whole population or area.

Choosing the right sampling method is one of the most important decisions in any geographical investigation. The method you choose will directly affect the reliability and validity of your results. In the Edexcel B exam, you will need to be able to describe different sampling methods, explain when each is appropriate, and evaluate their strengths and limitations.

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What Is Sampling?

Sampling is the process of selecting a subset of data points from a larger population. In geography, the "population" might be:

• All the pebbles on a beach
• All the people who visit a town centre on a Saturday
• Every 10-metre section of a river
• All the houses in a residential area
• Every tree in a woodland

Because we cannot measure the entire population, we select a sample and use it to make inferences (evidence-based conclusions) about the wider population.

Why Is Sampling Necessary?

Reason	Explanation
Time constraints	Fieldwork is typically limited to a few hours; measuring everything would take far too long
Cost	Equipment, travel and personnel are limited
Practicality	Some populations are too large or inaccessible to measure entirely
Safety	Some areas may be dangerous, so sampling reduces exposure to hazards
Data management	Smaller datasets are easier to process, present and analyse

Exam Tip: If asked why sampling is used, always link your answer to the specific context of the fieldwork. For example, "We used sampling because we could not measure every pebble on the 500m beach within our 3-hour fieldwork window."

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The Three Main Sampling Methods

There are three sampling strategies that you need to know for Edexcel B: random, systematic and stratified.

1. Random Sampling

In random sampling, every item in the population has an equal chance of being selected. Selection is determined by chance rather than by the investigator's choice.

How to carry out random sampling:

1. Define the area or population to be sampled
2. Use a random number generator (calculator, app or random number table) to generate coordinates or selection numbers
3. Go to the randomly selected location and take your measurement

Example: To measure pebble size on a beach, you could lay a grid over the beach using tape measures. Then use random number pairs to identify coordinates (e.g. 3,7 means 3 metres along and 7 metres up). Measure the pebble at each randomly generated coordinate.

Advantages:

• Eliminates personal bias because the investigator does not choose which items to measure
• Every item has an equal chance of selection, making the sample statistically fair
• Results can be tested for statistical significance because the sample is unbiased

Disadvantages:

• May produce an unrepresentative sample by chance (e.g. all random points might fall in one area of the beach)
• Can be impractical in the field — finding exact coordinates is time-consuming
• May miss important features or areas entirely
• Requires a way to generate truly random numbers

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2. Systematic Sampling

In systematic sampling, data is collected at regular intervals along a transect or across an area. The interval is decided in advance and remains constant throughout.

How to carry out systematic sampling:

1. Decide on the interval (e.g. every 10 metres, every 5th person, every 50 metres downstream)
2. Choose a starting point (this can be random to add an element of chance)
3. Collect data at each regular interval along the transect or throughout the area

Example: To investigate how vegetation changes from a sand dune to the back of a beach, you could place a tape measure from the shoreline to the back of the dunes and record plant species and percentage cover every 2 metres along the transect.

Advantages:

• Easy to set up and carry out — straightforward to follow regular intervals
• Provides good coverage of the whole area or population
• Ensures data is evenly spread, which is useful for identifying spatial patterns and gradients
• Quick and efficient, making it ideal for time-limited fieldwork

Disadvantages:

• May coincide with a regular pattern in the environment (e.g. if you measure every 10 metres and lamp posts are every 10 metres, you might always be measuring next to a lamp post)
• Does not eliminate bias if the starting point is not randomly chosen
• May miss localised variations between sampling points

Exam Tip: Systematic sampling is particularly useful when you expect a gradient or a pattern along a transect, such as river changes downstream or land use changes away from the CBD. If your investigation involves a transect, systematic sampling is almost always the best choice.

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3. Stratified Sampling

In stratified sampling, the population is first divided into sub-groups (strata) based on a known characteristic, and then samples are taken from each sub-group in proportion to their size.

How to carry out stratified sampling:

1. Identify the sub-groups within the population (e.g. different age groups, different zones of a town, different sections of a beach)
2. Determine the proportion each sub-group makes up of the total population
3. Allocate the number of samples to each sub-group according to its proportion
4. Within each sub-group, use random or systematic sampling to select individual items

Example: If you are surveying 100 people in a town and the population is 30% aged 16–30, 40% aged 31–55 and 30% aged over 55, you would survey 30 people from the 16–30 group, 40 from the 31–55 group and 30 from the over 55 group.

Advantages:

• Ensures all sub-groups are represented in the sample
• More representative than random sampling when the population contains distinct groups
• Reduces the chance of a biased sample that over-represents one group

Disadvantages: