Planning an investigation, gathering and processing raw data with uncertainties, and writing the project report that contributes 30 marks to the award.

What this key area is asking

The SQA wants you to know how to plan an experimental investigation, gather and process raw data with full uncertainty analysis, and write the report that makes up the Advanced Higher project, worth 30 marks and 25 per cent of the course award. It applies the inquiry skills of the whole course in one extended piece of work.

Planning the investigation

Good planning is where many marks are won or lost. The chosen experiment must have a genuine underpinning physics that you can explain, and the variables must be controlled so that any change in the dependent variable can be attributed to the independent one. Planning also means choosing instruments of appropriate precision and a range wide enough to reveal the relationship, and anticipating the main sources of uncertainty before starting.

Gathering and processing data

Raw data should be recorded in clear tables with units and the uncertainty of each measurement. Repeating reduces random uncertainty and lets you quote an uncertainty in each mean. Processing then combines measurements (often via a relationship rearranged into straight-line form), and the uncertainties are combined by adding percentage uncertainties for products and quotients. The aim is a final result quoted as a value with an absolute uncertainty.

Presenting and analysing results

A straight-line graph is the standard analytical tool: it tests whether the expected relationship holds and yields a quantity from its gradient. The analysis should compare the result with an accepted value where one exists, and discuss whether they agree within the quoted uncertainty. Clear, correctly labelled graphs with error bars are directly rewarded.

Evaluating and concluding

A strong evaluation does not just list problems; it identifies which uncertainty dominated (the largest percentage uncertainty) and proposes a specific improvement that would reduce it. The conclusion should answer the original aim quantitatively, with the result and its uncertainty, and connect the finding to the physics that motivated the investigation.

Examples in context

A pendulum project measures $g$ from the gradient of $T^2$ against $L$, repeating timings over many swings to cut random uncertainty. A terminal-velocity project drops spheres through a liquid and analyses the balance of forces. An RC discharge project measures the time constant of a capacitor and compares it with $RC$. In every case the marks come from a clear aim, valid data, correct uncertainty handling, good graphs and a justified evaluation, exactly the skills this area builds.

Try this

Q1. State what the independent variable in an experiment is. [1 mark]

• Cue. The variable deliberately changed by the experimenter.

Q2. State why measurements are repeated in the project. [1 mark]

• Cue. To take a mean, reducing random uncertainty and giving an uncertainty in the mean.

Q3. State what a good evaluation should identify about the uncertainties. [1 mark]

• Cue. The dominant (largest) source of uncertainty, with a justified improvement.