Use random sampling for biodiversity
Explain how random sampling reduces placement bias and supports an estimate of an area.
A Level; Cambridge International Biology 9700, 2028–2030. Written responses are self-checked; this does not certify supervised fieldwork or practical competence.
Before you begin
- Classification compares shared characteristics; populations occupy environments and contain variation.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Why choose quadrat positions randomly?
What you will learn
- Explain why sampling positions should be selected randomly
- Relate sampling design and replication to reliable estimates
Build the explanation
Random selection gives sampling locations an equal chance of inclusion, reducing the investigator’s tendency to choose unusually species-rich or convenient patches. Repeated samples across the defined area give an estimate of its biodiversity; sample size and coverage affect how representative that estimate is.
Use evidence within scope
Random sampling does not guarantee a perfect census or remove every source of error. Define the area and method, use enough samples, keep effort comparable and distinguish a sample estimate from the whole population.
Pause and explain
A random survey finds no rare species in 20 quadrats. Can the team conclude it is absent from the entire habitat?
Worked example
A researcher places every quadrat beside the path because it is convenient. What bias may result?
Show the worked solution
- The sampling positions are not randomly selected.
- Path-edge conditions may not represent the area.
Answer The sample may overrepresent path-edge conditions and be biased. Randomly selecting positions across the defined area would reduce this placement bias.
Try a topic test
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Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
A survey samples ten random coordinates in a meadow. Why is this more defensible than choosing ten visibly flower-rich patches?
Give me a hint
Identify the required evidence and sequence the reasoning.
Compare my reasoning
- Random positions reduce targeted placement.
- The sample is less likely to be chosen for a high count.
Random coordinates reduce investigator placement bias; deliberately choosing flower-rich patches would tend to overestimate flower abundance.
Look for these in your work
- Relevant evidence selected
- Reasoning linked to conclusion
Two teams use different quadrat sizes and sample numbers. Why may their abundance estimates be hard to compare?
Give me a hint
Use the supplied example or data to justify a conclusion.
Compare my reasoning
- Sampling effort and area differ.
- Keep method and effort comparable.
Different quadrat areas and numbers change sampling effort, so the estimates are not directly comparable without accounting for the design.
Look for these in your work
- Concept applied
- Limit or assumption stated
A random survey finds no rare species in 20 quadrats. Can the team conclude it is absent from the entire habitat?
Give me a hint
Apply the concept in a new context and qualify its limits.
Compare my reasoning
- A sample can miss rare or clustered organisms.
- Absence from the sample is not proof of absence from the area.
No. The species may be rare or patchily distributed and missed by the sample; increase representative sampling and report the detection limit.
Look for these in your work
- New context handled
- Conclusion appropriately bounded
Common mistakes
- Claiming random sampling removes all error
- Choosing sites after seeing where target organisms are abundant
What can you explain now?
Why choose quadrat positions randomly?
Compare with the explanation
To reduce selection or placement bias
Every eligible position should have a chance of selection.
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Make it stick
How does random placement improve a biodiversity survey?
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.