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Chemistry IA2 Data Analysis and Evidence Quality Examples

QCAA Chemistry IA2 examples for writing analysis, uncertainty, trendline and evidence-quality paragraphs using gases and galvanic cell experiments.

Updated 2026-05-22 · 4 min read

Quick answer

Strong Chemistry IA2 analysis does not just describe the graph. It explains the chemical relationship, uses trendline features as evidence, compares experimental and theoretical values, and judges validity and reliability using uncertainty, error bars, outliers and systematic error.

The examples below are adapted from two Sylligence-reviewed Chemistry IA2 drafts: a gases experiment and a galvanic cell experiment.

What the analysis section should do

The analysis section needs to move from result to meaning:

1. State the observed relationship.
2. Use numerical evidence from the graph or table.
3. Explain the relationship using chemistry theory.
4. Compare experimental values with theoretical expectations.
5. Judge reliability and validity using evidence-quality language.

Avoid spending the whole paragraph repeating table values. QCAA Chemistry IA2 analysis needs interpretation, not just description.

Gases IA2 analysis example

In the gases exemplar, the student plotted carbon dioxide volume at STP against calcium carbonate mass, then converted mass to moles and plotted volume against moles. This gave two useful analysis moves.

First, the volume-versus-mass graph showed a positive linear trend. That supported the expected proportional relationship: as calcium carbonate mass increased, moles of carbon dioxide produced also increased, so gas volume increased.

Second, the volume-versus-moles graph allowed the gradient to represent experimental molar volume. This was a stronger analysis choice because it turned the graph into a direct test of the chemical idea, not just a visual trend.

What the gases exemplar did in each section

| Section | What it did | |---|---| | Trend description | Identified a positive linear relationship between calcium carbonate mass and carbon dioxide volume | | Chemistry explanation | Linked the trend to Avogadro's law and the 1:1 mole ratio between calcium carbonate and carbon dioxide | | Gradient interpretation | Used the volume-versus-moles gradient as experimental molar volume | | Validity judgement | Compared the experimental gradient and y-intercept with theoretical expectations | | Reliability judgement | Used R-squared, error bars and repeated trials to discuss random error |

The stronger point was the use of graph features. A negative y-intercept did not just get reported; it was interpreted as possible systematic gas loss. That is how analysis becomes evidence quality.

Galvanic cell IA2 analysis example

In the galvanic cell exemplar, the student measured EMF as copper ion concentration increased. The raw graph showed EMF increasing at a decreasing rate, which supported a logarithmic relationship rather than a simple linear one.

The stronger move was linearisation. The student plotted EMF against the natural logarithm of the reaction quotient so the Nernst equation could be tested using a straight-line relationship.

This allowed the student to compare:

• experimental gradient against theoretical gradient
• experimental y-intercept against standard cell potential
• error bars against the theoretical trend
• R-squared value against reliability

That is much stronger than only saying "the EMF increased".

What the galvanic cell exemplar did in each section

| Section | What it did | |---|---| | Trend description | Identified that EMF increased as copper ion concentration increased | | Relationship type | Explained why the increase was logarithmic, not linear | | Linearisation | Re-plotted data using ln(Q) to test the Nernst equation | | Gradient comparison | Compared experimental gradient with theoretical Nernst gradient | | Intercept comparison | Interpreted a lower y-intercept as evidence of systematic EMF loss | | Evidence quality | Used uncertainty, R-squared and theoretical divergence to separate reliability from validity |

Validity versus reliability

Students often mix these up. These examples show the difference.

| Evidence | Usually supports | |---|---| | High R-squared value | Reliability or strong internal consistency | | Small spread between repeated trials | Reliability | | Error bars overlap the trendline | Reliability or random-error control | | Experimental values consistently below theoretical values | Validity concern | | Non-zero intercept where zero is expected | Validity concern | | Experimental gradient differs from theoretical gradient | Validity concern |

Reliable results can still be invalid. For example, the galvanic cell data could be very consistent across trials while still producing EMF values that were systematically too low because of circuit resistance.

Sentence patterns for evidence quality

Use sentence patterns like these:

• "The high R-squared value suggests the data points followed the trend consistently, supporting reliability."
• "The experimental y-intercept being below the theoretical value suggests a systematic loss, reducing validity."
• "Because the theoretical trend did not fall within the experimental uncertainty range, the difference is unlikely to be explained by random error alone."
• "The larger relative uncertainty at lower volumes means reliability was weaker for the smallest mass variations."
• "The linearised graph provides a stronger test of the expected relationship because the theoretical model predicts a straight-line relationship after transformation."

Use Sylligence for assignment feedback

Sylligence assignment feedback can help check whether your Chemistry IA2 analysis is doing more than describing a graph. It can flag where you need stronger links between the trendline, chemistry theory, uncertainty and evidence-quality judgement.

Sources

• QCAA Chemistry syllabus page
• QCAA Chemistry IA2 sample assessment instrument
• QCAA Chemistry 2025 subject report
• Chemistry LibreTexts: The cell potential/Electrochemistry/Voltaic_Cells/The_Cell_Potential)