S2.2.2—Processing and interpreting data

Syllabus
First assessment 2025
Objective
S2.2.2
Level
SL

Process Trends and Outliers

Process only what the research question needs

Show calculations from raw to processed values, then interpret the appropriate table, chart, diagram or graph. Separate what the representation displays from the physical explanation proposed for the pattern.

Evidence feature Defensible interpretation
Straight best-fit line through the origin within uncertainty Consistent with direct proportionality
Straight line with non-zero intercept Linear relationship, but not direct proportionality; investigate an offset
Curve or changing gradient Rate of change varies; a linear model is not supported
Area under a power-time graph Energy transferred, because power multiplied by time has energy units
Point far from the pattern Possible outlier; check procedure, uncertainty and repeats before deciding whether to include it

Justify inclusion or removal

Do not remove a point merely because it weakens the trend. Keep it unless there is a documented measurement or procedural reason to exclude it; where the cause is uncertain, compare the analysis with and without the point and state how the conclusion changes.

Worked data decision

For diameter readings 0.18,0.20,0.21,0.22,0.26,0.18mm0.18, 0.20, 0.21, 0.22, 0.26, 0.18\,\text{mm}, 0.26mm0.26\,\text{mm} is visibly separated from the cluster. If a documented reason supports exclusion, the mean of the remaining five is 0.198mm0.20mm0.198\,\text{mm}\approx0.20\,\text{mm}. Without that justification, report the alternative mean or discuss the point rather than hiding it.

S2.2.2 Exam Analysis

Assessment in practice

1 marks
How it is assessed

Questions explain how a graph supports PV=K or whether a T–d graph supports direct proportionality.

Command terms

Explain / Outline

What earns marks

Refer to the expected graph form and intercept, not just the presence of a trend.

Watch for

Calling a non-origin line direct proportionality or ignoring the fit when deciding whether data support a model.

Retrieve Data Evaluation

Record before judging

Keep qualitative observations, labelled quantitative data, units and uncertainty visible.

Test the model

Use the graph shape, intercept, fit, error bars and repeats to decide whether a relationship is supported and whether errors are random or systematic.