Scientific Method: Collection of Data (Cambridge (CIE) IGCSE Environmental Management): Revision Note
Exam code: 0680
Aims & hypothesis
Scientists follow a step-by-step method to make sure their data is trustworthy
This method helps them ask clear questions, plan fair tests, collect accurate results and make strong conclusions
In fieldwork, using a scientific method keeps the investigation fair, repeatable and reliable
Each stage builds on the one before it, like climbing a ladder to reach good scientific understanding
Suggest aims and hypotheses
Setting an aim gives the investigation a clear purpose, helping everyone understand what the study is trying to discover
A hypothesis is a testable idea, giving a prediction the investigation can prove or disprove
Writing a hypothesis helps focus the methods so data collected actually answers the aim
Hypotheses guide what variables you choose and how you measure change
Methods
Types of data
Data collected within research is primary data
Data collected by someone else but used in the enquiry is secondary data
Data which records quantities is quantitative data
Data which records descriptive information is qualitative data
Choosing a sampling strategy
Random or systematic sampling, helps avoid bias and makes results more representative
Picking sampling techniques
Quadrats or transects ensure data is gathered in the same way each time
Designing questionnaires and surveys
Collects people’s views clearly and consistently, improving reliability
Questionnaires can be used to gather a large sample of data
Surveys are more in-depth and tend to be used to gather a smaller data sample
Closed questions where answers are limited to single words, numbers or a list of options
Statements employ a scale to measure individuals' opinions. For example, strongly agree/agree
Open questions where the respondent can give any answer
Carrying out a pilot study
This helps test the method, revealing problems early so the final investigation runs smoothly
Good planning reduces mistakes and increases the accuracy of the whole study
Variables
A variable is a factor that can be changed in an experiment
Identifying measured variables makes it clear what data you are collecting and how you will record it
Choosing control variables keeps certain conditions the same, so changes in results can be linked to the factor you are testing
Control variables help ensure a fair test
Independent and dependent variables
The independent variable is the one you change on purpose to see what effect it has
The dependent variable is the one you measure, showing how the independent variable affects the system
Think of it as cause and effect
An independent variable is the cause, while a dependent variable is the effect
Use of repeats and replicates
Repeating measurements checks that the results are consistent and not just due to chance
Replicates at different locations or times give a wider set of results, increasing reliability and validity
More repeats help spot unusual results more easily, improving accuracy
Recording data
Recording data clearly and neatly helps avoid mistakes later when analysing results
Using tables, tally charts or digital devices keeps information organised and easy to understand
Good recording habits reduce the risk of losing data or misreading measurements
Analysis & conclusions
Analysing data helps identify patterns, trends or relationships between variables
Using averages, graphs or percentages makes results clearer and easier to compare
Data analysis turns raw numbers into meaningful scientific findings
Identify and process anomalies
Anomalies are results that do not fit the pattern, so spotting them helps improve reliability
Checking whether anomalies are caused by errors or natural variation strengthens the investigation
Removing or explaining anomalies helps produce more accurate averages and clearer trends
Form conclusions
A conclusion explains what the results show and whether they support the original hypothesis
Linking the conclusion back to the aim helps check whether the investigation achieved its purpose
Conclusions point out strengths, weaknesses and improvements, helping future studies become better
Examiner Tips and Tricks
In the exam, you may be asked how valid or reliable your conclusions were. Understanding the distinction between the two is important.
Reliability refers to whether the results you have gathered could be reproduced in the same conditions. For example, would a student measuring water quality at the same sample site in the same conditions obtain the same measurements?
Validity refers to the accuracy of the measure and whether the results represent what they are supposed to measure. For example, a measure of environmental quality should measure the environment and not economic characteristics.
Worked Example
A class wants to investigate how trampling affects plant cover in a school field
a) Suggest aims and hypotheses
Aim example:
To find out whether areas with more trampling have less plant cover
Hypothesis example:
Areas with high foot traffic will have lower plant cover because plants are damaged by frequent walking
Why this helps:
The aim gives the investigation direction and the hypothesis gives a prediction to test
b) Plan scientific methods
Sampling strategies
Example: Using random sampling by throwing a quadrat randomly across the field so every spot has an equal chance of being chosen
Sampling techniques
Example: Using a quadrat to estimate plant cover inside a 1 m² square so data is collected in the same way at each point
Questionnaires and surveys
Example: Asking students where they walk most often on the field to identify high-traffic zones
Pilot studies
Example: Testing the quadrat method in one small area first to see if plant cover is easy to estimate or if the quadrat needs a different size
Why this helps: The pilot shows if the method works before the real investigation starts
c) Measured and control variables
The percentage of plant cover inside each quadrat
Control variable examples
Using the same size quadrat every time
Collecting data on the same day when weather and light conditions are similar
Using the same person to estimate plant cover to keep the method consistent
d) Independent and dependent variables
Independent variable example
level of trampling (low, medium, high foot traffic areas)
Dependent variable example
Plant cover measured in percentage
Cause → effect
Changing the trampling level affects the amount of plant cover
e) Use of repeats and replicates
Repeats example:
Taking three readings in each area (low, medium, high trampling) to check consistency
Replicates example:
Repeating the same method in different parts of the field to reduce chance errors
Why this matters:
The more repeats you have, the more trustworthy your data becomes
f) Record data
Trampling level | Quadrat reading 1 | Quadrat reading 2 | Quadrat reading 3 | Average plant cover |
|---|---|---|---|---|
Low | 80% | 75% | 78% | 78% |
Medium | 50% | 55% | 52% | 52% |
High | 20% | 15% | 18% | 18% |
Why this helps:
Organised tables make it easier to analyse patterns later
g) Analyse data
As trampling increases, plant cover declines
To illustrate the declining trend, students can create a line graph
The pattern is more evident when averages are calculated
h) Identify and process anomalies
In the high-trampling area, one quadrat shows 40% plant cover while the others show about 15%
Possible reasons:
Someone estimated incorrectly
The quadrat landed on a patch recently reseeded
Processing the anomaly:
Repeat the measurement
Decide whether to exclude the anomalous value if it was caused by error
i) Form conclusions
Plant cover is lower in areas with high trampling, which supports the hypothesis
The investigation shows a clear negative relationship between trampling and vegetation
Improvements for next time might include:
Using more quadrats
Testing on different days
Measuring other factors like soil moisture
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