The IB Chemistry IA criteria divide the scientific investigation into four equally weighted areas: Research Design, Data Analysis, Conclusion, and Evaluation. Each criterion is worth 6 marks, producing a total of 24 marks. To reach the highest markband, you must do more than include the correct sections: you must explain methodological decisions, process data accurately, interpret uncertainty, and connect every conclusion and improvement to evidence.
Under the Chemistry course first assessed in 2025, the scientific investigation contributes 20% of the final Chemistry grade at both SL and HL. The official IB Chemistry subject brief specifies approximately 10 hours for the investigation and a maximum report length of 3,000 words.
How the IB Chemistry IA is assessed
The scientific investigation is marked by your teacher and then subject to external IB moderation. The same criteria apply at SL and HL, so HL students do not receive a different rubric, although the chemistry and analytical methods should still be appropriate to the investigation being conducted.
Criterion
Maximum marks
IA weighting
Central question
Research Design
6
25%
Is the question contextualized and the methodology justified and reproducible?
Data Analysis
6
25%
Is the data recorded, processed, and presented clearly, precisely, and accurately?
Conclusion
6
25%
Does the conclusion answer the question using the analysis and accepted chemistry?
Evaluation
6
25%
Are specific limitations evaluated and realistic improvements explained?
4.7
X
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Total
24
100%
Each criterion uses broad achievement bands of 1-2, 3-4, and 5-6 marks. Work in the highest band consistently explains and justifies rather than merely stating or describing. A polished report can still lose marks if the evidence required by a descriptor is absent.
The revised course places 50% of the IA marks in Conclusion and Evaluation, as emphasized in the IB’s Chemistry curriculum update. Students should therefore avoid spending most of the word count on background theory and method while treating the final sections as an afterthought.
Research Design criterion: 6 marks
Research Design assesses how effectively you communicate the purpose and practical design used to address the research question. At the highest level, the question has a specific and appropriate chemical context, the methodological choices are explained, and the procedure allows the investigation to be reproduced.
What examiners reward
A strong research question normally identifies:
The independent and dependent variables, or two correlated variables in a database investigation
The chemical system being investigated
A measurable outcome and, where useful, the analytical technique
A range or important conditions that define the investigation
For example, “How does temperature affect reaction rate?” is too broad. A more focused question would ask how changing temperature from 298 K to 318 K affects the initial rate of the acid-catalysed iodination of propanone, measured using colorimetry under specified concentration conditions.
Your background should explain only the chemistry needed to understand the design and later interpret the results. Relevant equations, collision theory, rate laws, equilibrium principles, or spectroscopic relationships are useful when they directly inform the method. A long textbook-style introduction does not compensate for an unfocused question.
Methodological considerations should explain why you selected the range, intervals, number of repeats, measuring equipment, control variables, and data-processing approach. For every important control variable, state how it was controlled and why uncontrolled variation would affect the dependent variable.
The final method must be sufficiently precise to reproduce. Include quantities, concentrations, apparatus precision, timing decisions, preparation procedures, and relevant safety, ethical, or environmental considerations. RevisionDojo’s guide to writing a clear and reproducible IA methodology can help distinguish useful operational detail from unnecessary narration.
Common Research Design mistakes
Stating a topic instead of a focused research question
Listing controls without explaining their chemical significance
Choosing ranges or intervals without justification
Omitting how solutions were prepared or measurements were taken
Writing a procedure that describes intentions rather than what was actually done
Adding generic safety statements that do not address the chemicals and quantities used
Data Analysis criterion: 6 marks
Data Analysis assesses whether data has been recorded, processed, and presented in ways that address the research question. Top-band work must be both clear and precise, consider uncertainties appropriately, and contain accurate, relevant processing without significant omissions.
What examiners reward
Present raw quantitative data in organized tables with:
Qualitative observations that help interpret the chemistry
Processing must transform raw measurements into evidence capable of answering the question. Depending on the investigation, this may involve means, rates, concentrations, calibration curves, gradients, percentage differences, equilibrium constants, or statistical tests. Include at least one worked example so the route from raw to processed data is transparent.
Uncertainty treatment should match the method. This may require instrumental uncertainties, uncertainty propagation, error bars, gradient uncertainties, or comparison between uncertainty and observed change. Merely listing apparatus uncertainties does not demonstrate that their implications were considered.
Graphs need numbered captions, labelled axes with units, suitable scales, and an appropriate model or best-fit line. Do not force a linear trend when chemical theory predicts a curve. Discuss anomalies rather than silently deleting them, and justify any alternative treatment of an outlier.
Reporting processed values without showing how they were calculated
Using inconsistent significant figures or missing units
Including graphs that do not help answer the research question
Treating a high correlation coefficient as proof of causation
Ignoring uncertainty when interpreting gradients or literature values
Removing anomalous data without scientific justification
Conclusion criterion: 6 marks
Conclusion assesses whether you answer the research question consistently with your analysis and compare the result with accepted scientific context. A top-band conclusion is justified, not simply stated.
Begin with a direct answer to the research question. Support it using specific processed values, trends, gradients, uncertainties, or statistical results. If temperature increased a rate constant, for example, quantify the increase and explain whether the change was large relative to experimental uncertainty.
Next, interpret the pattern using relevant chemistry. Explain whether the result agrees with a theoretical model, accepted value, published trend, or chemical principle. Accepted scientific context may come from traceable journal articles, reputable databases, textbooks, or course materials, but the source must be cited precisely enough to locate it.
A difference from literature does not automatically invalidate the investigation. Evaluate whether the experimental and accepted values overlap within uncertainty, calculate a percentage difference where appropriate, and consider whether methodological conditions make a direct comparison valid.
Avoid introducing new processing in the conclusion. Calculations needed to justify the answer belong in Data Analysis, while the conclusion should interpret those results.
Evaluation criterion: 6 marks
Evaluation assesses your discussion of methodological weaknesses and limitations and the improvements proposed in response. For the highest marks, identify specific issues, explain their relative impact, and connect each one to a realistic improvement.
A useful evaluation distinguishes between:
Random effects, which increase scatter and reduce precision
Systematic effects, which may shift measurements consistently in one direction
Control limitations, where another variable changes with the independent variable
Scope limitations, such as a narrow range or assumptions that restrict the conclusion
For each limitation, explain the mechanism of impact. Saying “heat was lost” is incomplete. Explain where heat was transferred, whether this would make the measured temperature change too small, how it affects the calculated enthalpy, and whether the effect is likely to be substantial.
Improvements must directly address the identified weakness. “Use better equipment” is vague, while “replace the measuring cylinder with a 25.00 cm³ volumetric pipette to reduce volume uncertainty” is specific and technically plausible. Repeating trials improves confidence in a mean but does not correct a systematic calibration error.
Prioritize the weaknesses that most strongly affect the conclusion. A short evaluation of three consequential limitations is usually more effective than a long list of minor human errors. RevisionDojo’s guide to writing an evidence-based IA evaluation offers a useful structure for connecting limitation, impact, and improvement.
What score is needed for a 7?
The IA does not independently determine your final subject grade because it contributes 20% alongside the externally assessed papers. Component boundaries can also vary between examination sessions.
As a practical benchmark, a grade-7 level Chemistry IA typically sits around 20-22 marks out of 24, with 20/24 often appearing near the lower edge of the top component band in recent guidance. This is a target rather than a permanent official guarantee. Aim for consistent evidence across all four criteria instead of assuming that a polished introduction or sophisticated experiment will compensate for weak analysis.
A plausible target profile is 5/6 in every criterion, producing 20/24. Since the criteria are equally weighted, improving an underdeveloped Evaluation from 3 to 5 can matter as much as refining Research Design from 5 to 6.
Final IB Chemistry IA checklist
Research Design
The research question identifies measurable variables and a specific chemical system.
Background chemistry directly supports the design and expected relationship.
Range, intervals, repeats, controls, and measurement methods are justified.
The procedure contains enough quantitative detail to be reproduced.
Relevant safety and environmental issues are addressed specifically.
Data Analysis
Raw and processed tables have titles, headings, units, and consistent precision.
One clear example is shown for each important type of calculation.
Uncertainties are recorded, propagated, and interpreted where appropriate.
Graphs use suitable models, labels, error bars, and fit information.
Anomalies are retained, discussed, and treated transparently.
Conclusion and Evaluation
The conclusion directly answers the research question with numerical evidence.
Results are interpreted with their associated uncertainties.
Comparisons with accepted scientific context are relevant and traceable.
Each major limitation includes its direction, magnitude, or effect on validity.
Every improvement is realistic and linked to a named limitation.
The four IB Chemistry IA criteria reward a connected scientific argument. Your design must generate relevant evidence, your analysis must process that evidence accurately, your conclusion must interpret it against accepted chemistry, and your evaluation must explain how the method shaped the result.
Aiming for at least 5 marks per criterion is more reliable than trying to compensate for one weak section. After completing your own rubric check, compare the structure with IB Chemistry IA exemplars and use the RevisionDojo Chemistry IA Grader, powered by Jojo AI, for criterion-aligned formative feedback before consulting your teacher.
Daniel holds an MSc in Chemistry from Imperial College London and has taught IB Chemistry for over 20 years, including as Head of Chemistry. His focus is on building the conceptual understanding behind each equation rather than rote recall.
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