Scientific Inquiry, Measurement, and Data Analysis
Core concepts
Understand the scientific method: hypothesis, controlled variables, independent and dependent variables, reliability and validity. Know how to read and plot graphs, identify systematic versus random error, estimate uncertainty, and use significant figures. Distinguish accuracy (closeness to true value) from precision (repeatability). Recognise the difference between a hypothesis, a theory and a law, and the role of peer review.
Common traps
- Accuracy vs precision are often swapped in distractors; a precise set of readings can be inaccurate if there is systematic bias.
- Systematic errors shift all readings in one direction (e.g., zero error); random errors scatter and are reduced by averaging.
- Line of best fit should not simply connect first and last points.
- Significant figures in a calculated answer should reflect the least precise input.
- Confusing correlation with causation in data interpretation.
Worked example
Five readings of a length: 2.51, 2.49, 2.50, 2.52, 2.48 cm. Mean = 2.50 cm. The spread (±0.02 cm) indicates random uncertainty; if the true value were 2.60 cm, the measurements would be precise but not accurate - evidence of systematic error.
Rule of thumb
When analysing data, state the variables, plot the graph, draw a best-fit line, and quote uncertainty with the correct units and significant figures.