How is AS 1 Experimental Techniques assessed?

It is an internally assessed unit. There is no terminal written paper. Instead you build a portfolio of practical work that your centre marks against CCEA assessment criteria, and a sample is moderated by CCEA. The portfolio shows planning, safe practical work, data collection and processing, presentation and evaluation, so it rewards the skills the written units also test through data questions.

What is the difference between reliability and validity?

Reliability is how consistently a measurement can be repeated: close repeat readings are reliable. Validity is whether the investigation actually measures what it set out to measure, which depends on changing only the independent variable and holding the control variables constant. An investigation can be reliable but not valid, for example if it repeats well but a control was allowed to change so it measures the wrong thing.

How do I work out the uncertainty in a measurement?

For a single reading from an analogue or digital instrument, the absolute uncertainty is taken as plus or minus half the smallest scale division (the resolution). Percentage uncertainty is the absolute uncertainty divided by the reading, times 100. Reading larger values lowers the percentage uncertainty, and repeating a measurement and averaging reduces the effect of random error on the mean.

What separates a random error from a systematic error?

A random error causes scatter on both sides of the true value, shows up as spread in repeat readings, and is reduced by repeating and averaging. A systematic error shifts every reading by the same amount in the same direction, for example an uncalibrated balance or a consistent parallax error, and repeating does not remove it because the offset is always there. Only re-calibration or a change of method removes a systematic error.

How do I write a high-mark evaluation?

Comment specifically on reliability (how close the repeats were and whether anomalies were present), on validity (whether the controls held so the conclusion is sound), and on the main sources of error, classifying each as random or systematic. Then propose realistic, named improvements, such as using apparatus with finer resolution, taking more values across the range, or controlling temperature with a thermostatic water bath. Avoid vague phrases like be more careful.

Northern IrelandLife & Health Sciences

CCEA Life and Health Sciences AS 1 Experimental Techniques: planning, data handling and evaluation for the practical portfolio

A guide to the CCEA Life and Health Sciences AS 1 Experimental Techniques portfolio unit: how it is internally assessed, how to plan a fair test, control variables and risk, process data with units, significant figures and uncertainty, present results, and write a high-mark evaluation.

Generated by Claude Opus 4.814 min readCCEAUpdated 2026-06-16

Reviewed by: AI editorial process; not yet individually human-reviewed

Jump to a section

What this unit demands
Planning a fair test
Collecting and processing data
Presenting and evaluating
How this unit is examined
Check your knowledge

What this unit demands

AS 1 Experimental Techniques is the practical-skills foundation of CCEA Life and Health Sciences. It is internally assessed, so your centre marks a portfolio of practical work against CCEA criteria and CCEA moderates a sample. There is no written paper for this unit, but the skills it builds (planning, data handling, presentation and evaluation) are tested again in the data questions of the externally assessed units, so it is worth mastering early.

This overview pulls the portfolio skills together. The matching dot-point page works through each skill with practice questions; this guide ties them into one workflow you can apply to any investigation.

Planning a fair test

A strong plan opens with a testable hypothesis: a prediction of how the dependent variable changes with the independent variable, justified by the underlying science. You then identify the independent variable (the one factor you change), the dependent variable (what you measure), and every control variable (factors kept constant so the test is fair). Choosing a wide range of at least five values for the independent variable lets you see a clear trend.

A risk assessment is part of the marked plan: for each hazard you give the risk and a control measure. Hazards in this subject often include hot water baths, corrosive acids, biological materials and sharp glassware, so goggles, gloves and aseptic technique appear frequently.

Collecting and processing data

Raw readings go in a clear table: independent variable first, repeat readings of the dependent variable, then a calculated mean. Resolution is the smallest change the instrument shows; the uncertainty in a single reading is half the resolution. Percentage uncertainty is that absolute uncertainty over the reading, times 100, and it falls as readings get larger. Calculated results are quoted to a sensible number of significant figures, matching the least precise measurement, and every quantity carries its SI unit. Anomalous repeats are identified and excluded from the mean, with a reason.

Presenting and evaluating

Processed data are graphed with the independent variable on the x axis, suitable scales using more than half the grid, labelled axes with units, accurate points and a line or curve of best fit. The trend is then described and explained with the relevant science.

The evaluation carries the top marks. Separate random errors (scatter, reduced by repeats) from systematic errors (a consistent offset, not removed by repeats). Judge reliability from the closeness of repeats and validity from whether the controls held, then propose specific, realistic improvements.

How this unit is examined

Although AS 1 itself is a portfolio, the same skills appear in the written units as:

Variables and planning. Identifying independent, dependent and control variables and explaining why controls keep a test fair.
Data processing. Calculating means, rates, percentage change and percentage uncertainty with correct units and significant figures.
Graphs. Plotting, drawing best-fit lines and reading off or calculating gradients (including tangents for initial rate).
Evaluation. Classifying errors as random or systematic and suggesting valid improvements.

Check your knowledge

A mix of skill questions covering the unit. Attempt them under timed conditions, then check against the solutions.

Distinguish between the independent, dependent and control variables in an investigation. (3 marks)
State what is meant by the resolution of an instrument. (1 mark)
A reading of 12.0 cubic centimetres is taken with apparatus of resolution 0.5 cubic centimetres. Calculate the percentage uncertainty. (2 marks)
Explain why repeating a measurement does not remove a systematic error. (2 marks)
Give two features of a well-drawn results graph. (2 marks)
State one way to improve the reliability and one way to improve the validity of an investigation. (2 marks)
Define a fair test. (2 marks)
Explain why an anomalous result should be excluded before calculating a mean. (2 marks)

Solutions

Q1: The independent variable is the one factor deliberately changed; the dependent variable is what is measured in response; the control variables are all other factors kept constant so the test is fair.
Q2: The resolution is the smallest change in a quantity that an instrument can detect or display (its smallest scale division).
Q3: Half the resolution is 0.25; percentage uncertainty is $0.25 / 12.0 \times 100 = 2.1\%$ .
Q4: A systematic error shifts every reading by the same amount in the same direction, so averaging more readings keeps the same offset; only re-calibration or a changed method removes it.
Q5: Any two of: labelled axes with units, scales using more than half the grid, accurately plotted points, a suitable line or curve of best fit.
Q6: Reliability: repeat readings and take a mean (or use finer apparatus). Validity: control all other variables so only the independent variable changes.
Q7: A fair test changes only the independent variable while holding all control variables constant, so any change in the dependent variable can be attributed to the independent variable.
Q8: An anomaly does not fit the trend of the repeats and is likely caused by a one-off error; including it would distort the mean and the conclusion, so it is excluded with a reason.

Sources & how we know this

CCEA GCE Life and Health Sciences specification — CCEA (2016)