S2.2 Collecting and processing data
- Syllabus
- First assessment 2025
- Topic
- S2.2
- Level
- HL
Learning objectives
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
This exam question is unavailable.
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
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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
This exam question is unavailable.
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.