Data Interpretation (Cambridge (CIE) AS Environmental Management)
Revision Note
Written by: Alistair Marjot
Reviewed by: Bridgette Barrett
Interpreting Data
Understanding and interpreting data is crucial for a successful experiment
It helps confirm if your findings agree or disagree with your initial hypothesis
This process gives meaning to the information or data that has been collected and helps identify why it's important
Sometimes, the data might only partially support the initial hypothesis
Scientific Method Limitations
Scientific experiments and investigation often present challenges that make it tough to get reliable data
These challenges come from things like how the research is set up, the materials used, the methods followed, and dealing with limited time and money
Common problems include issues with the sample, its size, and difficulties with the tools, instruments or methods used for collecting information
These factors can all contribute to unreliable data and uncertainty in the results
Limitations of environmental research can include:
Challenges accessing remote or hazardous locations for repeated data collection
Unpredictable weather
The difficulties of sorting and organising samples when out in the field
It's important that scientists clearly communicate the challenges they face during their research and suggest ways to improve or fix them
Other challenges to collecting reliable data can include:
Human error: when the researcher makes mistakes e.g. miscounting the number of oxygen bubbles produced by an aquatic plant as photosynthesis occurs
Researcher bias: scientists might expect a certain answer and focus only on that, which can make them miss other information that could give a different result - being aware of and reducing these biases is crucial for fair and accurate science
Scientific Theory
A robust scientific hypothesis, backed by substantial data, can progress into a scientific theory
For example, the initial hypothesis that deforestation reduces biodiversity has now evolved into a theory with a huge amount of supporting evidence
Theories serve as the basis for predictive models, enabling scientists to predict outcomes in different environmental scenarios
For example, theories about habitat size and fragmentation can now help environmental scientists to predict the consequences of continued deforestation on biodiversity
It is important to note that scientific theories are not fixed
They have the flexibility to adjust and transform as new data emerges
This adaptability ensures that theories stay accurate, reflecting the latest understanding of environmental processes
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