Original application practice · 19 indicative marks
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Eight non-overlapping 0.50 m² quadrats were placed at randomly selected positions across a 2000 m² field. A plant count and soil-moisture reading were recorded at each position. The field boundary defines the sampling area.
| Quadrat | Plant count | Soil moisture / % |
|---|---|---|
| 1 | 2 | 20 |
| 2 | 0 | 15 |
| 3 | 5 | 35 |
| 4 | 1 | 22 |
| 5 | 4 | 32 |
| 6 | 0 | 18 |
| 7 | 3 | 30 |
| 8 | 1 | 25 |
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Original self-review guidance, not an official mark scheme. Other scientifically valid responses may express the same ideas.
The eight quadrats cover 4 m² and contain 16 plants. Estimated density is 4 plants m⁻², giving 8000 plants in 2000 m².
Alternatives: Scaling 16 plants from 4 m² of sampled area to 2000 m² is an equivalent method. The result assumes the sample represents the defined field.
This is an estimate from a sample, not an exact census.
Total count = 16, so mean count = 16/8 = 2 plants per quadrat. Density = 2/0.50 = 4 plants m⁻². Estimated field total = 4 × 2000 = 8000 plants, assuming the sample adequately represents the defined field.
Higher moisture is generally associated with more plants. The 15% and 18% quadrats contain none, while 32% and 35% contain four and five. The counts are not exactly proportional to moisture.
Alternatives: A correct scatter-plot description is acceptable. Counts at 22% and 25% are both one; the relationship is not a fixed plants-per-percentage ratio.
Do not state statistical significance without an appropriate test and result.
Higher moisture readings are generally associated with larger counts: the 15% and 18% quadrats have no plants, while those at 32% and 35% contain four and five. The pattern is not perfectly proportional; for example, the counts at 22% and 25% are both one.
The counts suggest an association, but the quadrats were not assigned different watering treatments. Other conditions may covary with moisture and influence the plants. The table therefore does not predict how much abundance would change after watering.
Alternatives: A controlled watering investigation is a valid way to explain what further evidence is needed. Other plausible field confounds are acceptable when linked to both moisture and abundance.
Insufficient causal evidence does not prove that water has no effect.
The observations show an association, but moisture was not independently manipulated. Light, nutrients or disturbance could covary with moisture and affect abundance. A controlled comparison varying moisture while making other relevant factors comparable would better test its causal effect. The field table alone does not predict the size of that effect.
Run replicated transects down the slope. Sample fixed-area quadrats at pre-set distances, recording plant counts and environmental conditions at each. Calculate density and plot it against distance. If the observations are suitably independent, Spearman’s rank can test a monotonic association between distance and abundance.
Alternatives: A belt transect or a justified stratified design across slope positions can be valid. Spearman’s rank can assess a monotonic association between position and abundance; a named test must match the collected variables. Credit defensible alternatives rather than requiring identical wording.
A single transect may leave observations spatially dependent. A test name alone does not resolve that limitation or demonstrate causation.
Random placement gives field positions a defined chance of inclusion and reduces deliberate choice of plant-rich patches. Enough independent samples are needed to assess spatial variation. A slope pattern instead calls for position-based sampling along a justified transect, measuring both abundance and relevant environmental conditions.
Take more independent random quadrats and report the spread of the counts. This samples spatial variation more fully and supports a better estimate of the mean and its uncertainty.
Alternatives: A justified increase in total sampled area or representative stratification can earn the method point. Recounting existing quadrats addresses observer error, but does not add new information about spatial variation.
More quadrats do not automatically remove a biased selection method.
Increase the number of independently placed random quadrats and report the distribution of counts or a justified uncertainty estimate. This better assesses spatial variation and the precision of the estimated mean. It does not make every field location identical or turn 8000 into an exact census.
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Selected OCR A H420 connections: 4.2.1, 6.3.1, 6.3.2. Use official papers for examination-board assessment.