S2.2 Collecting and processing data
- Syllabus
- First assessment 2025
- Topic
- S2.2
- Level
- HL
Record Experimental Data
Record what happened, not what was expected
Capture relevant qualitative observations alongside sufficient quantitative readings. Qualitative evidence may explain a change, threshold or anomaly; quantitative evidence establishes the size and spread of the relationship.
| Record | Include | Why it matters |
|---|---|---|
| Raw data table | Variable names, units, instrument resolution/uncertainty and unrounded readings | Preserves the original evidence for later processing |
| Repeats | Every reading, not only the mean | Reveals spread and possible anomalies |
| Qualitative observation | What was seen, heard or changed, linked to the relevant reading | Provides context for interpreting the numbers |
| Collection issue | What occurred, when, affected values and action taken | Makes a repeat, correction or exclusion auditable |
Address issues while preserving the evidence
If a sensor saturates, a timing event is missed or the setup changes, pause and check the method. Repeat the affected measurement under the stated conditions when possible, but keep the original entry identified rather than silently replacing or adjusting it.
Use consistent precision
Record to the precision supported by the instrument and keep units explicit. Enough data means sufficient range, spacing and repeats to reveal the relationship—not a large table of duplicated or invented values.
S2.2.1 Exam Analysis
Assessment in practice
1 marksHow it is assessed
Questions ask learners to plot a missing data point accurately.
Command terms
Draw
What earns marks
Place the point at the correct coordinates within the stated plotting tolerance and preserve the graph’s scale.
Watch for
Plotting the point in the wrong quadrant or ignoring the graph scale.
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 0.18,0.20,0.21,0.22,0.26,0.18mm, 0.26mm is visibly separated from the cluster. If a documented reason supports exclusion, the mean of the remaining five is 0.198mm≈0.20mm. Without that justification, report the alternative mean or discuss the point rather than hiding it.
S2.2.2 Exam Analysis
Assessment in practice
1 marksHow 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.
Assess Data Quality
Data can be precise without being accurate—and accurate-looking data can come from an invalid test
Assess each quality separately against the intended measurement and research question.
| Quality | Question to ask | Evidence or improvement |
|---|---|---|
| Accuracy | How close is the result to an accepted or well-supported value? | Calibration, correction of systematic offset or comparison with a reference |
| Precision | How closely do repeated readings agree, or how small is the measurement resolution/uncertainty? | Smaller spread and finer justified resolution |
| Reliability | Are results consistent across sufficient repeats or repeated trials? | Repeat under the same conditions and compare the pattern |
| Validity | Does the method isolate and measure the intended relationship? | Appropriate controls, range, method and interpretation |
Repeats target random variation
Repeating a drop-time measurement under the same conditions can reveal spread and improve precision of a representative result. Saying only “find an average” is incomplete: the reason is to reduce the influence of random variation, not to guarantee accuracy.
Systematic effects need a changed method
A zero offset can shift every reading together, so tightly clustered repeats may still be inaccurate. Calibration or correction can address the offset; averaging the same biased method cannot. Validity can also fail even when readings are precise and reliable if another variable causes the observed change.
S2.2.3 Exam Analysis
Assessment in practice
1 marksHow it is assessed
Questions explain why one value per condition is poor or identify systematic error from a graph.
Command terms
Suggest / Identify
What earns marks
Mention random variation/outliers and the need for multiple measurements, or identify the non-origin trend as systematic evidence.
Watch for
Saying one trial is poor only because it is less precise, without linking it to random variation or outliers.
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.