Investigating Phenomena Scientifically 🧠

Key Concepts:

• Scientific Investigations: Careful data collection and analysis.

  • Stages: Hypothesis, predictions, experiments, conclusions.

  • 📊 Remember: Follow the steps methodically.

Hypotheses and Predictions:

• 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:

• 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.

Data Collection:

• 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.

Risk Assessment:

• 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.

Data Processing and Presentation 🧠

• After experimenting to investigate a hypothesis, there is a process of collecting, presenting, and analysing data.

Processing 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.

• 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.

Presenting Data:

• 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:

• 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.

Experiment Improvements:

• 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.

Drawing Conclusions and Scientific Development 🧠

Drawing Conclusions:

• 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 and Causation:

• 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.

Modification of Scientific Theories:

• 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:

• Peer review: Scientific community checks new findings.

  • Ensures validity before acceptance.

Models and Limitations:

• 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).

Scientific and Technological Impact on Society 🧠

Benefits of Science

• Improve quality of life:

  • Vaccinations.

  • Antibiotics

  • Fertilisers for crops.

  • Water can be processed to be made potable.

Risks

• 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.

Ethical issues

• Some scientific explanations can have ethical implications.

  • 🧬Example: use of embryonic stem cells, genetic engineering.

Communicating science

• Scientists must communicate their work in a way that can be understood by a large range of audiences.

Tips for Remembering Confusing Concepts 🧠

General Tips:

• 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.

Scientific Investigations:

• 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.

Data Processing:

• 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 and Theories:

• 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.

Practical Examples:

• 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.