| Lesson Plan |
| Grade: |
Date: 25/02/2026 |
| Subject: Biology |
| Lesson Topic: use the Hardy–Weinberg principle to calculate allele and genotype frequencies in populations and state the conditions when this principle can be applied (the two equations for the Hardy–Weinberg principle will be provided, as shown in the Mathematica |
Learning Objective/s:
- Describe the Hardy–Weinberg principle and its five assumptions.
- Calculate allele (p, q) and genotype (p², 2pq, q²) frequencies from observed data.
- Apply the principle to determine whether a population is evolving.
- Explain how violations of each assumption lead to specific evolutionary mechanisms.
- Interpret genotype‑frequency patterns to infer types of selection.
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Materials Needed:
- Projector and screen
- Whiteboard and markers
- Handout with Hardy–Weinberg equations and example problem
- Calculators or spreadsheet software
- Worksheets for practice problems
- Coloured beads or cards for allele‑frequency simulation (optional)
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Introduction:
Begin with a quick poll: “If you could choose any trait in a pet, would you breed for it?” linking everyday breeding to selection. Review prior knowledge of alleles and genotype frequencies, then state that by the end of the lesson students will be able to calculate p, q and test for equilibrium.
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Lesson Structure:
- Do‑Now (5’) – Short question on how selection changes traits; collect responses.
- Mini‑lecture (10’) – Review Hardy–Weinberg assumptions and equations with slides.
- Guided example (12’) – Work through the beetle population problem, calculate p, q and expected frequencies together.
- Group activity (15’) – Teams use worksheets or bead simulation to calculate frequencies for a new dataset and check equilibrium.
- Concept check (5’) – Quick quiz (Kahoot/handout) on the five conditions.
- Extension discussion (8’) – Link deviations to types of selection using a flowchart.
- Summary & exit ticket (5’) – Students write one condition and one consequence of its violation.
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Conclusion:
Recap that the Hardy–Weinberg equation provides a baseline for detecting evolution and that each assumption protects that baseline. For the exit ticket, learners note which condition they think is hardest to meet in real populations. Homework: complete a worksheet calculating frequencies for a provided population dataset.
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