Practical requirements
Reliable measurement and the practical work on density, free fall, forces, thermal transfer and resistance.
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Before you begin
There is no separate practical exam: you carry out at least four of these practical tasks in class, and the theory papers test the skills behind them. The lessons start with the skills every investigation needs: measuring and recording, drawing and using graphs, and evaluating a method. They then work through the density, free-fall, forces, thermal and resistance investigations. Each one prepares the method and the analysis; the readings themselves need supervised laboratory time.
Be comfortable with: measurement, because every reading needs a suitable instrument and unit, and D.C. circuits, for the resistance investigations.
Learning goals
- Measurements of length, mass, temperature, time interval, volume of liquids/solids and force (e.g. weight) using appropriate instruments
Syllabus statements covered
- Measurements of length, time interval, volume, mass and weight, temperature, current and voltage using the listed instruments
- Determination of the density of a liquid, or of a regularly or irregularly shaped solid that sinks in water
- Determination of the value of the acceleration of free fall
- Investigation of the effects of balanced and unbalanced forces
- Investigation of the factors affecting transfer of energy by thermal processes
- Determination of the resistance of a circuit component
Lessons
Work through them in order.
- Measuring and recording practical dataChoose instruments, avoid reading errors, time complete oscillations and investigate repeats before recording valid data.
- Graphs and data analysisProcess pendulum readings, draw a best-fit line, use a large gradient triangle and judge what the graph supports.
- Evaluating investigationsLink each limitation to its effect and a specific improvement, then propose a modification to the method.
- Density investigationsChoose volume methods for liquids and solids, calculate density from matching mass and volume readings, and explain measurement errors.
- Free-fall investigationEstimate g from an electronic-timing graph and evaluate the distance, release and timing assumptions.
- Balanced and unbalanced forces investigationTest steady motion, keep the total moving mass fixed in a trolley–hanger experiment and interpret acceleration and friction evidence.
- Thermal transfer investigationPlan a fair cooling comparison, draw and compare cooling curves, and evaluate how well the variables were controlled.
- Resistance of a componentConnect the meters, record paired readings, interpret a resistance graph and control heating and reading errors.
- Wire length and resistanceMove a wire contact, measure between the contacts, control heating and use ratios and a graph to test the length relationship.
Practise and check
Topic reference
Quick reference
| Skill | What to do |
|---|---|
| choosing an instrument | pick one with a suitable range and the finest useful scale; check its zero first |
| reading a scale | look straight at it to avoid parallax |
| repeating readings | repeat, look for anomalies and take the mean of the consistent values |
| recording | table headings show quantity / unit; values have consistent decimal places |
| graphs | label both axes with quantity / unit, use most of the grid and draw a best-fit line |
| gradient | use a large triangle on the best-fit line, not two data points |
| evaluating | name a limitation, say how it affects the result, then give a matching improvement |
Core knowledge to remember
- In a fair test, change one independent variable, measure the dependent variable and keep the other variables constant.
- Repeat readings to reduce the effect of random errors. A systematic error, such as a zero error, is not reduced by repeating.
- A conclusion must be supported by the data, and stated within the range tested.
Common mistakes
- Recording readings without units, or to different numbers of decimal places.
- Ignoring a zero error, or trying to remove a systematic error by repeating readings.
- Calculating a gradient from two data points instead of a large triangle on the best-fit line.
- Giving a vague improvement, such as “be more careful”, instead of one that fixes a named limitation.