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Home Subjects Environmental Science Data analysis & evaluation

Data Analysis and Evaluation

Environmental Science

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Area of Study

How is scientific inquiry used to investigate contemporary environmental challenges?

Unit

Unit 4: How can climate change and the impacts of human energy use be managed?

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Data Analysis and Evaluation

Environmental Science
01 May 2026

Organising, Analysing and Evaluating Primary Data

Once primary data has been collected, it must be organised systematically, analysed to identify patterns, and evaluated for sources of error and uncertainty before valid conclusions can be drawn.

Step 1: Organising Data

Raw data must be organised to be interpretable:

Data Recording and Tables

  • Record data directly into a pre-designed data table at time of collection (not from memory later)
  • Table should include: column headers with units; replication (multiple measurements per condition)
  • Use consistent significant figures throughout

Example data table structure:

Site Quadrat Species A Species B Species C Total N SID
Forest A 1 5 3 7 15 0.67
Forest A 2 4 2 6 12 0.66
… … … … … … …

Calculations

  • Calculate derived quantities (SID, efficiency, averages) from raw data
  • Show all workings clearly
  • Use consistent significant figures

Step 2: Presenting Data

Graphs

Choose the appropriate graph type:

Data Type Appropriate Graph
Continuous IV; continuous DV (e.g. time vs. temperature) Line graph
Categorical IV; continuous DV (e.g. habitat type vs. SID) Bar graph
Two continuous variables being compared Scatterplot
Composition (% of total) Pie chart (use sparingly)

Graph conventions (VCAA standard):
- Title (include IV and DV)
- Labelled axes with units
- Appropriate scale
- Data points or bars clearly shown
- Error bars if replication data available
- Legend if multiple data series

Identifying Patterns

Types of patterns to identify:
- Linear relationship: DV increases/decreases proportionally with IV
- Non-linear relationship: Curve, exponential, threshold effects
- Cyclical pattern: Seasonal, daily, annual rhythms
- Correlation: Direction (positive/negative) and strength
- No relationship: No systematic change in DV with IV

Step 3: Statistical Analysis

Measures of Central Tendency

  • Mean: Average value — sensitive to outliers
  • Median: Middle value — robust to outliers
  • Mode: Most frequent value

Measures of Spread

  • Range: Maximum minus minimum
  • Standard deviation: Average distance of values from the mean — indicates precision

Comparing Groups

  • If means of two groups differ, is the difference likely to be real or due to chance?
  • Overlap in standard deviation ranges suggests results may not be significantly different

Step 4: Evaluating Sources of Error and Uncertainty

Types of Error

Error Type Source Effect on Data
Systematic error Calibration; consistent procedural bias Shifts all values in one direction; affects accuracy
Random error Natural variation; measurement variability Scatter around true value; affects precision
Human error Observer bias; measurement mistakes Variable effects; may be systematic or random

Common Sources in Environmental Investigations

Source Example
Sampling bias Non-random quadrat placement favouring species-rich areas
Observer effect Animals fleeing before being counted
Instrument limitations pH meter not calibrated; thermometer imprecision
Environmental variability Survey day was unusually hot; data not representative
Small sample size Five quadrats is insufficient to represent a large, variable habitat
Temporal limitations One season of data does not capture year-round variation

Expressing Uncertainty

  • Standard deviation (SD) and standard error (SE) quantify measurement uncertainty
  • Identifying the direction of bias (likely over- or under-estimate) is more valuable than just acknowledging uncertainty
  • Example: ‘Non-random quadrat placement in visually dense vegetation likely overestimates species diversity for the broader site’

Step 5: Drawing Conclusions

A valid conclusion:
- Directly answers the research question
- Is supported by specific data (cite values, trends)
- Acknowledges limitations that constrain confidence
- Avoids overclaiming (no data ‘proves’ anything)
- Links back to the hypothesis

EXAM TIP: When asked to identify sources of error, state: (1) the specific error, (2) the mechanism by which it affects the data, and (3) the direction of its effect (is the result likely higher or lower than reality?). A common VCAA error is listing ‘human error’ as a source without specifying what kind and how it affects the result.

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