Unit 7: Ideas About Science (Biology B OCR GCSE)
Scientific Investigations: Careful data collection and analysis.
Stages: Hypothesis, predictions, experiments, conclusions.
📊 Remember: Follow the steps methodically.
Hypothesis: Possible explanation for an observation.
A hypothesis must be made before any data can be collected and analysed.
📝 Example: “Plants need nutrients to grow. Fertiliser provides nutrients.”
Prediction: Testable statement based on the hypothesis.
The prediction will state how the effect of a factor will affect the outcome.
🧩 Example: “If fertiliser is added, basil seedlings will grow taller.”
Experiments are carried out to test the prediction.
Variables:
Independent Variable: What you change (e.g., light intensity).
Dependent Variable: What you measure (e.g., photosynthesis rate).
Control Variables: Kept constant for a fair test (e.g., temperature).
🔬 Example: Measuring bubbles in pondweed photosynthesis experiment.
Fair Test: Control variables must be kept constant.
Tools: Stopwatch, water bath for temperature control.
When planning an experiment, you must decide what data needs to be collected and what measurements will be taken.
Depending on the type of investigation, this may include choosing a sample size or range of values that will be measured.
Sample Size: Large enough for valid conclusions, small enough to be manageable.
Method: Clear, concise, repeatable.
🧩Example: Use a measuring cylinder for 1 cm³ accuracy.
Once the data type is chosen and the variables are outlined, the experiment method can be written.
Apparatus list: Appropriate equipment must be selected.
The equipment must be suitable for the job and must provide precise, valid, and accurate data.
Hazard: something that could cause harm.
Risk: the chance that the hazard will cause harm.
Hazards and Risks:
Identify and reduce risks (e.g., wear safety glasses with irritants).
⚠️ Example: Hydrogen peroxide safety.
After experimenting to investigate a hypothesis, there is a process of collecting, presenting, and analysing data.
Significant Figures:
Consistent in all measurements.
🔢 Tip: Round to the lowest significant figure in calculations.
SI Units: Standard units (e.g., second, meter, kilogram).
You need to be able to convert between units, as some equations will require measurements in specific units.
Quantity | SI Base Unit |
---|---|
Time | Second, s |
Length | Metre, m |
Energy | Joule, J |
Mass | Kilogram, kg |
Errors:
Random Errors: Environmental changes, worn instruments.
Systematic Errors: Consistent inaccuracies.
Anomalous Results: Do not fit the trend, exclude if justified.
🔍 Example: Anomalous bubble count in photosynthesis experiment.
Tables: Drawn with a ruler, units in headers.
📊 Example: Mean height of plants in cm.
Bar Charts:
For categorical data, easy to compare.
Graphs: For continuous data, plot points and draw best-fit lines.
Continuous data: data that can be measured and can have any value within a range.
📈Example: Graph of gas produced over time.
Range Bars:
Indicate uncertainty or variation.
Gradient Calculation: Rate of reaction from graph slope.
🔍Example: (Change in y) ÷ (Change in x).
Extrapolation: continuing the trend further to obtain more data points just outside the range.
Interpolation: constructing new data points within the range of known data points using a line of best fit.
Statistics give values that can be easily compared across a range of experiments.
Range: Spread of data (largest - smallest).
Mean: Average value (sum of values ÷ number of values).
📉 Tip: Exclude anomalies.
Fair Test Evaluation:
Was the method valid? Was it a fair test? Were all variables controlled? Were there anomalous values? Was there enough evidence to reach a valid conclusion?
🔄 Suggestions: Narrow intervals for more accuracy.
🌱Example: Test enzyme activity at 35°C, 40°C, 45°C.
Data Analysis:
Conclude based on data only.
Pattern recognition and correlation.
Individual cases do not provide convincing evidence for or against correlation.
📈Example: Conclude enzyme A is more effective if data supports it.
Correlation: Relationship between variables.
🧩 Example: Smoking correlated with lung cancer, but not always causative.
Causation: Direct cause-and-effect relationship.
One event is the result of the occurrence of the other event.
Correlation doesn’t always mean causation.
📉 Example: CO₂ levels causing global temperature rise.
New Evidence: Leads to theory modification.
The proposition of a scientific explanation involves creative thinking.
🧬 Example: Darwin's theory supported by modern genetics.
Technological Advances: Enable better evidence and theory adjustments.
🌱Example: Antibiotic resistance studies.
Scientific theory: a general explanation that can be applied to numerous situations.
Peer review: Scientific community checks new findings.
Ensures validity before acceptance.
Models are used to help explain ideas and to test explanations quickly.
It represents the main features of a system and can be used to predict possible outcomes.
Types of Models:
Representational (visual), descriptive (explanatory), mathematical (predictive).
🧩 Example: Lock-and-key enzyme model's limitations.
Benefits and Risks:
Science improves quality of life (e.g., antibiotics, fertilisers).
Consider ethical implications (e.g., stem cell research).
Improve quality of life:
Vaccinations.
Antibiotics
Fertilisers for crops.
Water can be processed to be made potable.
Some applications of science can risk people’s quality of life or the environment.
Everything carries a certain level of risk.
People are generally happier to accept a risk if it is something they choose to do, rather than imposed.
People often have an idea of a perceived risk, which can differ from a calculated risk.
Perceived risk: an overestimate of the risk.
Some scientific explanations can have ethical implications.
🧬Example: use of embryonic stem cells, genetic engineering.
Scientists must communicate their work in a way that can be understood by a large range of audiences.
Create Mnemonics:
Use acronyms or phrases to remember lists (e.g., “HOMES” for Great Lakes: Huron, Ontario, Michigan, Erie, Superior).
Visual Aids:
Draw diagrams or mind maps to visualise relationships and processes.
Teach Someone Else:
Explaining concepts to others can reinforce your understanding.
Stages Order:
Hypothesis, Prediction, Experiment, Conclusion (H-PEC).
🌱 Hypothesis: Plants need nutrients.
🌱 Prediction: Fertiliser helps growth.
🧪 Experiment: Add fertiliser.
📊 Conclusion: Analyse results.
Variables:
Independent (I change), Dependent (Data), Control (Constant).
🔧 Independent: Thing you change.
📏 Dependent: Thing you measure.
🛠️ Control: Things you keep the same.
Significant Figures:
Think “Consistent Figures” to remember consistency.
✏️ Consistent in all calculations.
Errors:
Random: R for Random events, Systematic: S for Same every time.
🎲 Random: Fluctuates.
📉 Systematic: Consistent error.
Graph Rules:
Dependent (Vertical), Independent (Horizontal) (D-I-V-H).
📈 Dependent: y-axis (Vertical).
📊 Independent: x-axis (Horizontal).
Models Types:
Representational (R), Descriptive (D), Mathematical (M).
📘 Representational: Physical analogy.
📖 Descriptive: Specific explanation.
🔢 Mathematical: Data patterns.
Causation vs. Correlation:
🔗 Causation: Direct link. Cause-Effect.
📊 Correlation: Relationship, not cause.
Correlation doesn’t always mean causation.
Hypothesis Example:
Nutrients and fertiliser link: Think N-F Link (Nutrients-Fertiliser).
🌱 Hypothesis: Nutrients boost growth.
🌿 Prediction: Fertilised plants grow better.
Graph Example:
Remember Bubbles for Photosynthesis: B-P (Bubbles-Photosynthesis).
💧 Hypothesis: Light affects photosynthesis.
🌞 Prediction: More light, more bubbles.
Scientific Investigations: Careful data collection and analysis.
Stages: Hypothesis, predictions, experiments, conclusions.
📊 Remember: Follow the steps methodically.
Hypothesis: Possible explanation for an observation.
A hypothesis must be made before any data can be collected and analysed.
📝 Example: “Plants need nutrients to grow. Fertiliser provides nutrients.”
Prediction: Testable statement based on the hypothesis.
The prediction will state how the effect of a factor will affect the outcome.
🧩 Example: “If fertiliser is added, basil seedlings will grow taller.”
Experiments are carried out to test the prediction.
Variables:
Independent Variable: What you change (e.g., light intensity).
Dependent Variable: What you measure (e.g., photosynthesis rate).
Control Variables: Kept constant for a fair test (e.g., temperature).
🔬 Example: Measuring bubbles in pondweed photosynthesis experiment.
Fair Test: Control variables must be kept constant.
Tools: Stopwatch, water bath for temperature control.
When planning an experiment, you must decide what data needs to be collected and what measurements will be taken.
Depending on the type of investigation, this may include choosing a sample size or range of values that will be measured.
Sample Size: Large enough for valid conclusions, small enough to be manageable.
Method: Clear, concise, repeatable.
🧩Example: Use a measuring cylinder for 1 cm³ accuracy.
Once the data type is chosen and the variables are outlined, the experiment method can be written.
Apparatus list: Appropriate equipment must be selected.
The equipment must be suitable for the job and must provide precise, valid, and accurate data.
Hazard: something that could cause harm.
Risk: the chance that the hazard will cause harm.
Hazards and Risks:
Identify and reduce risks (e.g., wear safety glasses with irritants).
⚠️ Example: Hydrogen peroxide safety.
After experimenting to investigate a hypothesis, there is a process of collecting, presenting, and analysing data.
Significant Figures:
Consistent in all measurements.
🔢 Tip: Round to the lowest significant figure in calculations.
SI Units: Standard units (e.g., second, meter, kilogram).
You need to be able to convert between units, as some equations will require measurements in specific units.
Quantity | SI Base Unit |
---|---|
Time | Second, s |
Length | Metre, m |
Energy | Joule, J |
Mass | Kilogram, kg |
Errors:
Random Errors: Environmental changes, worn instruments.
Systematic Errors: Consistent inaccuracies.
Anomalous Results: Do not fit the trend, exclude if justified.
🔍 Example: Anomalous bubble count in photosynthesis experiment.
Tables: Drawn with a ruler, units in headers.
📊 Example: Mean height of plants in cm.
Bar Charts:
For categorical data, easy to compare.
Graphs: For continuous data, plot points and draw best-fit lines.
Continuous data: data that can be measured and can have any value within a range.
📈Example: Graph of gas produced over time.
Range Bars:
Indicate uncertainty or variation.
Gradient Calculation: Rate of reaction from graph slope.
🔍Example: (Change in y) ÷ (Change in x).
Extrapolation: continuing the trend further to obtain more data points just outside the range.
Interpolation: constructing new data points within the range of known data points using a line of best fit.
Statistics give values that can be easily compared across a range of experiments.
Range: Spread of data (largest - smallest).
Mean: Average value (sum of values ÷ number of values).
📉 Tip: Exclude anomalies.
Fair Test Evaluation:
Was the method valid? Was it a fair test? Were all variables controlled? Were there anomalous values? Was there enough evidence to reach a valid conclusion?
🔄 Suggestions: Narrow intervals for more accuracy.
🌱Example: Test enzyme activity at 35°C, 40°C, 45°C.
Data Analysis:
Conclude based on data only.
Pattern recognition and correlation.
Individual cases do not provide convincing evidence for or against correlation.
📈Example: Conclude enzyme A is more effective if data supports it.
Correlation: Relationship between variables.
🧩 Example: Smoking correlated with lung cancer, but not always causative.
Causation: Direct cause-and-effect relationship.
One event is the result of the occurrence of the other event.
Correlation doesn’t always mean causation.
📉 Example: CO₂ levels causing global temperature rise.
New Evidence: Leads to theory modification.
The proposition of a scientific explanation involves creative thinking.
🧬 Example: Darwin's theory supported by modern genetics.
Technological Advances: Enable better evidence and theory adjustments.
🌱Example: Antibiotic resistance studies.
Scientific theory: a general explanation that can be applied to numerous situations.
Peer review: Scientific community checks new findings.
Ensures validity before acceptance.
Models are used to help explain ideas and to test explanations quickly.
It represents the main features of a system and can be used to predict possible outcomes.
Types of Models:
Representational (visual), descriptive (explanatory), mathematical (predictive).
🧩 Example: Lock-and-key enzyme model's limitations.
Benefits and Risks:
Science improves quality of life (e.g., antibiotics, fertilisers).
Consider ethical implications (e.g., stem cell research).
Improve quality of life:
Vaccinations.
Antibiotics
Fertilisers for crops.
Water can be processed to be made potable.
Some applications of science can risk people’s quality of life or the environment.
Everything carries a certain level of risk.
People are generally happier to accept a risk if it is something they choose to do, rather than imposed.
People often have an idea of a perceived risk, which can differ from a calculated risk.
Perceived risk: an overestimate of the risk.
Some scientific explanations can have ethical implications.
🧬Example: use of embryonic stem cells, genetic engineering.
Scientists must communicate their work in a way that can be understood by a large range of audiences.
Create Mnemonics:
Use acronyms or phrases to remember lists (e.g., “HOMES” for Great Lakes: Huron, Ontario, Michigan, Erie, Superior).
Visual Aids:
Draw diagrams or mind maps to visualise relationships and processes.
Teach Someone Else:
Explaining concepts to others can reinforce your understanding.
Stages Order:
Hypothesis, Prediction, Experiment, Conclusion (H-PEC).
🌱 Hypothesis: Plants need nutrients.
🌱 Prediction: Fertiliser helps growth.
🧪 Experiment: Add fertiliser.
📊 Conclusion: Analyse results.
Variables:
Independent (I change), Dependent (Data), Control (Constant).
🔧 Independent: Thing you change.
📏 Dependent: Thing you measure.
🛠️ Control: Things you keep the same.
Significant Figures:
Think “Consistent Figures” to remember consistency.
✏️ Consistent in all calculations.
Errors:
Random: R for Random events, Systematic: S for Same every time.
🎲 Random: Fluctuates.
📉 Systematic: Consistent error.
Graph Rules:
Dependent (Vertical), Independent (Horizontal) (D-I-V-H).
📈 Dependent: y-axis (Vertical).
📊 Independent: x-axis (Horizontal).
Models Types:
Representational (R), Descriptive (D), Mathematical (M).
📘 Representational: Physical analogy.
📖 Descriptive: Specific explanation.
🔢 Mathematical: Data patterns.
Causation vs. Correlation:
🔗 Causation: Direct link. Cause-Effect.
📊 Correlation: Relationship, not cause.
Correlation doesn’t always mean causation.
Hypothesis Example:
Nutrients and fertiliser link: Think N-F Link (Nutrients-Fertiliser).
🌱 Hypothesis: Nutrients boost growth.
🌿 Prediction: Fertilised plants grow better.
Graph Example:
Remember Bubbles for Photosynthesis: B-P (Bubbles-Photosynthesis).
💧 Hypothesis: Light affects photosynthesis.
🌞 Prediction: More light, more bubbles.