| Lesson Plan |
| Grade: |
Date: 25/02/2026 |
| Subject: Biology |
| Lesson Topic: use Spearman’s rank correlation and Pearson’s linear correlation to analyse the relationships between two variables, including how biotic and abiotic factors affect the distribution and abundance of species (the formulae for these correlations will b |
Learning Objective/s:
- Describe key biotic and abiotic factors influencing species distribution and abundance.
- Explain the difference between Pearson’s r and Spearman’s ρ and when each is appropriate.
- Calculate Pearson’s linear correlation and Spearman’s rank correlation from given data sets.
- Interpret correlation coefficients in ecological contexts and discuss their limitations.
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Materials Needed:
- Projector and screen
- Laptop with spreadsheet software (Excel or Google Sheets)
- Printed data set handouts
- Calculator or scientific calculator app
- Whiteboard and markers
- Worksheet with formulae and interpretation guide
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Introduction:
Begin with a quick discussion of how environmental factors shape where species live. Ask students to recall examples of temperature or predator effects on abundance, then outline that today they will learn to quantify these relationships using correlation analysis. Success will be demonstrated by correctly computing and interpreting both Pearson’s r and Spearman’s ρ for a sample data set.
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Lesson Structure:
- Do‑now (5') – Students list biotic and abiotic factors that could affect a pond species.
- Mini‑lecture (10') – Review concepts of correlation, linear vs. monotonic relationships, and formulae.
- Guided example (15') – Walk through calculating Pearson’s r for the temperature‑abundance data using the spreadsheet.
- Paired activity (15') – Students compute Spearman’s rank correlation for the same data and compare results.
- Interpretation discussion (10') – Groups interpret the coefficients in ecological terms and identify potential limitations.
- Exit ticket (5') – Write one sentence summarising when to choose Pearson vs. Spearman.
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Conclusion:
Summarise that strong positive correlations indicate a likely link between temperature and abundance, but do not prove causation. Students complete an exit ticket and are assigned homework to find a real‑world data set and apply both correlation methods. Review key steps on the next lesson.
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