What you will learn
- how to turn a vague curiosity into an investigable question and a testable hypothesis,
- how to identify independent, dependent and controlled variables in an experiment,
- how to plan a fair test, recognise risks, and choose suitable equipment,
- how to represent data using tables, line graphs, bar graphs and scatter plots,
- how to analyse results, identify anomalies, and write an evidence-based conclusion.
Worked example 0 Real-world example: does fertiliser make plants grow taller?
Question: How does the amount of fertiliser added to soil affect the height of bean plants after 4 weeks?
- Hypothesis: If more fertiliser is added, the plants will grow taller (up to a point), because plants use minerals from fertiliser to build tissue.
- Variables:
- Independent (what I change): amount of fertiliser (0, 5, 10, 20 g per pot).
- Dependent (what I measure): plant height in cm after 4 weeks.
- Controlled (kept the same): same bean variety, same pot size, same soil, same light, same water, same start date.
- Method: pots per fertiliser level (total pots). Measure weekly. Record carefully.
- Data: average height per treatment group — plot on a bar graph or line graph.
- Conclusion: if taller plants appeared with more fertiliser up to some level, and no taller with more, this supports the hypothesis within that range.
Key idea: a fair test changes one thing at a time while keeping everything else constant. This is what lets you say “the fertiliser caused the difference.”
1. Questions and hypotheses
A good investigable question is answerable by measurement. Examples:
- Weak: “Are plants healthy?” (vague, no variables).
- Better: “How does the amount of light affect the height of a sunflower?”
A hypothesis is a testable prediction that links a cause to an effect. A common structure:
If [I change X], then [Y will do Z], because [scientific reason].
2. Variables
- Independent variable (IV): the one thing the experimenter deliberately changes. Usually drawn on the x-axis.
- Dependent variable (DV): the measurement that may respond. Drawn on the y-axis.
- Controlled variables (CVs): everything else that could affect the DV, kept the same.
Worked example 1 Identifying variables
Question: Does the type of drink bottle affect how long water stays cold in the sun?
- IV: the type of bottle (stainless steel, plastic, glass).
- DV: temperature of the water after hours (°C).
- CVs: starting water temperature, starting volume, position in the sun, time of measurement, room/outdoor conditions, thermometer used.
Key idea: every CV you miss is a possible alternative explanation that weakens your conclusion.
3. Planning a fair test
Key steps before you start:
- State the aim, IV, DV and CVs clearly.
- Plan multiple readings (replicates) so you can average and spot anomalies.
- Choose equipment that gives the right precision (a cm ruler for plant height, a digital thermometer to °C).
- Identify and manage risks: hot liquids, glassware, chemicals, electricity. State the safety measure.
- Check for ethics if using living things: no harm, appropriate numbers.
4. Recording data
A clear table has headings with units, independent variable on the left, and space for repeats.
| Fertiliser (g) | Height trial 1 (cm) | Height trial 2 (cm) | Height trial 3 (cm) | Mean (cm) |
|---|---|---|---|---|
| 0 | 12 | 13 | 12 | 12.3 |
| 5 | 18 | 17 | 19 | 18.0 |
| 10 | 24 | 23 | 25 | 24.0 |
| 20 | 22 | 21 | 20 | 21.0 |
5. Graphs
- Line graph: when both variables are numerical and continuous (e.g. time vs temperature).
- Bar graph: when the IV is a category (e.g. type of bottle, brand of battery).
- Scatter plot: when you have pairs of measurements and want to see correlation.
- Pie chart: for proportions of a whole.
6. Analysis and conclusion
An anomaly is a data point that does not fit the trend. Possible causes: measurement error, a disturbed specimen, a real effect (e.g. too much fertiliser is harmful). Report anomalies — do not silently drop them.
A conclusion links back to the hypothesis using the evidence.
Worked example 2 Writing a conclusion
Results: the data in the table above.
- “As fertiliser increased from to g, mean plant height increased from to cm — roughly doubling.”
- “At g, mean height dropped to cm — this is an anomaly relative to the trend.”
- “The results support the hypothesis up to g of fertiliser. Above this, extra fertiliser may damage the plants, so the hypothesis is only partly supported.”
- “Limitations: small sample, only one variety of bean, only weeks — repeat with more plants over longer time.”
Key idea: conclusions state what the evidence shows, what it does not show, and what uncertainties remain.
7. Errors, limitations and evaluating
- Random errors: small fluctuations (thermometer reading to nearest 0.5°C). Reduce by taking the mean of repeats.
- Systematic errors: consistent bias (a misaligned scale). Reduce by calibrating equipment.
- Limitations: things your design could not control (weather changing between test days).
Worked example 3 Spotting an unfair test
A student times how long different ice blocks take to melt. Block A sits in a warm kitchen; block B sits on a shaded bench outside.
- IV is supposed to be the ice block itself — but the location is also different.
- Temperature, light and air flow are not controlled.
- Any difference in melt time could be due to the location, not the block.
To fix: melt all blocks in the same environment, one at a time or side by side.
Practice: Year 7
Tier 1: recall and identify
- Define: investigable question, hypothesis, independent variable, dependent variable, controlled variable.
- Write a hypothesis for: “Does the height of a ramp affect how far a toy car rolls after leaving it?”
- In the experiment above, identify IV, DV and two CVs.
- State two reasons to repeat a measurement.
- Which type of graph should you use for: (a) temperature over time, (b) brand of battery vs total run time, (c) height vs weight of classmates?
- What is an anomaly?
- What is a risk assessment, and why is it needed?
- Name one random error and one systematic error in measuring liquid volume.
- What is a control group?
- State one reason why sample size matters.
Tier 2: explain and reason
- Explain the difference between an observation and an inference.
- Explain why controlling variables is essential for drawing cause-and-effect conclusions.
- Why is a hypothesis written before data is collected?
- A student presents only their three “best” data points. Explain why this is poor science.
- Explain why a larger sample size makes a conclusion more reliable.
- A friend says, “ice cream causes shark attacks, because both rise in summer.” Identify the logical error.
Tier 3: apply to a novel context
- Design a fair test to answer: “Does the colour of a drink bottle affect how quickly water inside heats in the sun?” State IV, DV, at least four CVs, and the number of replicates.
- A student records the following lengths of plant shoots (cm): . Identify the anomaly and suggest two possible causes.
- Sketch (in words, not on paper) the shape of a graph you would expect for water temperature over 20 minutes as an ice cube melts and then warms in a beaker. State what the y-axis and x-axis show.
- A class tests four brands of paper towel to see which absorbs most water. Describe a procedure with a clear IV, DV, three CVs and a replicate count.
Challenge
Harder reasoning
- A study finds that students who eat breakfast perform better on tests. Does this prove that eating breakfast causes better performance? Suggest two alternative explanations and describe how a better study could tell them apart.
- A class measures the boiling point of water on five different hotplates and gets readings of , , , , °C. Which is most likely anomalous? Calculate the mean with and without it, and decide which is a better estimate of the true value.
- You want to know if a new fertiliser really works. Explain why you need a control group (no fertiliser) and why simply comparing “before” and “after” on the same plants is not enough.
- Design a simple investigation to test whether the length of a pendulum affects its swing period. State IV, DV, CVs, procedure, and what a graph of length vs period would look like.