AQA GCSE · Question 11.2 · Data Collection and Sampling Methods
Give one reason why Fynn doesn't wait a lot longer than two weeks.
How to approach this question
1. **Consider the assumptions of the method again**: The method assumes a "closed" population, meaning the total number of individuals does not change significantly between the first and second samples.
2. **Think about long time periods**: What happens to an animal population over several months or a year?
* **Births**: New, untagged squirrels are born.
* **Deaths**: Some squirrels (both tagged and untagged) will die.
* **Migration**: Squirrels might move into (immigration) or out of (emigration) the forest.
3. **How this affects the estimate**: All these factors change the total population size and the proportion of tagged animals, violating the core assumption and making the estimate inaccurate.
4. **Formulate the reason**: A long waiting period is avoided to minimise the effects of population changes (births, deaths, migration).
Full Answer
To ensure there are no significant changes in the population size due to births, deaths, immigration, or emigration during the study period.
Another key assumption of the capture-recapture method is that the population remains closed during the period of the study. This means there should be no significant changes to the population size due to:
- **Births** (which would add more untagged individuals)
- **Deaths** (which would remove both tagged and untagged individuals)
- **Immigration** (new untagged individuals arriving)
- **Emigration** (tagged and untagged individuals leaving)
If Fynn waits too long, these events are more likely to occur, changing the population size and the proportion of tagged animals within it. This would violate the assumption and lead to an inaccurate estimate. The two-week period is chosen as a balance – long enough for mixing, but short enough to assume the population is relatively stable.
Common mistakes
✗ Irrelevant answers like "the tags might fall off" (while possible, the population change is the key statistical reason).
✗ Vague answers like "things might change".
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