IB Chemistry IA Examples

The IB Chemistry Internal Assessment is a 10-hour scientific investigation worth 20% of your final grade. It is marked on the same five criteria as other Group 4 IAs — Research design, Data analysis, Conclusion and Evaluation, and Communication — but Chemistry brings specific challenges: quantitative data is expected, error calculations are required, and examiners have a low tolerance for vague methodology. This guide covers what high-scoring IB Chemistry IA examples look like, which investigation types work best, and where marks are most commonly lost.

How the IB Chemistry IA Is Marked

• Research design (6 marks): A focused research question in a genuine context — a non-standard variable or an original data source makes the design convincingly your own.
• Exploration (6 marks): Clear research question, relevant background theory, correct identification of variables (independent, dependent, controlled), and a reproducible methodology.
• Analysis (6 marks): Quantitative data processing (means, uncertainties, graphs), correct use of units, and a conclusion supported by the data. Error propagation is expected at HL.
• Evaluation (6 marks): Specific assessment of methodological limitations, systematic and random errors, and concrete improvements. The most under-scored criterion.
• Communication (4 marks): Clear structure, appropriate scientific notation, correct citations, and appropriate length (6–12 pages).

IB Chemistry IA Topics That Score Well

1. Effect of concentration on reaction rate (colorimetry)

Rate kinetics is a core HL and SL topic. A well-executed version uses a colorimeter to measure absorbance at regular intervals, processes the data to determine rate constants, and — at HL — determines the order of reaction with respect to the varying reactant. The personal engagement mark comes from choosing a reaction with a real-world context: the bleaching of food dyes, the oxidation of ascorbic acid, or the iodine clock with household starch.

What separates a 7 from a 5: Processing the data to produce a rate law expression (rate = k[A]ⁿ), including uncertainty bars on graphs, and discussing whether the deviation from expected order is due to temperature fluctuation or the colorimeter's detection limit.

2. Titration-based investigations

Acid-base or redox titrations are reliable but need a non-textbook angle to make the context convincingly personal, strengthening Research design. Strong examples include: determining the vitamin C content of different apple varieties across a ripening period; comparing acidity of commercial kombucha brands; measuring iron(II) content in iron supplement tablets before and after air exposure. The methodology is standard — the originality comes from the context.

For Analysis, calculating the percentage uncertainty of each piece of equipment (burette, pipette) and propagating those through to the final result is expected. Students who report only "the percentage error was 3%" without showing the calculation source lose Analysis marks.

3. Effect of temperature on equilibrium position (Le Chatelier's principle)

Investigating the equilibrium between NO₂ and N₂O₄, or the cobalt(II) chloride equilibrium in different solvents, allows a student to measure colour change quantitatively using a colorimeter and apply Le Chatelier's principle. A strong version compares experimental equilibrium constants at different temperatures with literature values for ΔH, and discusses why the observed shift matches (or doesn't match) the exothermic/endothermic prediction.

4. Electrochemistry — cell potential investigations

Measuring electrochemical cell potentials using different metal electrodes or concentrations allows for comparison with standard electrode potentials from data tables. Using the Nernst equation at HL to predict how cell potential should vary with concentration, then comparing this to measured values, produces rich data for both Analysis and Evaluation. Personal engagement comes from choosing electrode combinations with a practical context (batteries in consumer electronics, corrosion of specific metals).

5. Chromatography and separation science

Paper chromatography or TLC to identify components of natural dyes, food colouring, or plant pigments is accessible and visually clear. The quantitative measure is the Rf value; a strong IA compares calculated Rf values to literature values across different solvent systems, discusses polarity effects on separation, and proposes which solvent system would be optimal for a specific application.

The Most Common Reasons Chemistry IAs Score Below a 6

Insufficient replicates

Chemistry examiners expect a minimum of five trials per condition to calculate a meaningful standard deviation. Three replicates produce a standard deviation that is statistically unreliable. If you have five conditions (five concentrations, five temperatures) × five replicates, that is 25 data points — achievable in a 10-hour IA. Students who run three replicates because they ran out of time are losing Analysis marks that are very easy to earn.

Random errors treated as systematic errors (or vice versa)

A common Evaluation error is writing "there were errors in my measurements" without distinguishing between random error (scatter around the mean, reduced by averaging) and systematic error (consistent bias in one direction, not fixed by averaging). A colorimeter that was not zeroed correctly produces systematic error. Temperature fluctuations during titration produce random error. Each requires a different improvement — and examiners can tell whether the student understands the distinction.

Conclusions that don't reference the data

"The results supported the hypothesis" is not a conclusion. "The reaction rate constant k increased from 0.023 s⁻¹ at 25°C to 0.091 s⁻¹ at 45°C, consistent with the Arrhenius equation prediction and within the range reported by [source]" is a conclusion. Every number in your conclusion should be traceable to a row in your data table.

Evaluation that lists errors without quantifying their effect

Examiners want to know the direction and magnitude of each limitation's effect. "The water bath fluctuated by ±1.5°C. Since the rate constant is exponentially sensitive to temperature (from the Arrhenius equation), this represents approximately a 12% variation in rate constant at the temperatures used, which explains the scatter visible in the graph at higher temperatures" — that is developed evaluation. "The temperature was not perfectly controlled" is not.

Uncertainty Calculations: What's Actually Required

IB Chemistry explicitly requires uncertainty propagation. The basics:

• Record absolute uncertainty for every instrument (e.g., burette: ±0.05 cm³ per reading, so ±0.10 cm³ per titre).
• For addition/subtraction: add absolute uncertainties.
• For multiplication/division: add percentage uncertainties.
• Report final results with appropriate significant figures and absolute uncertainty.
• Compare your percentage uncertainty to your percentage error (difference between experimental and literature values). If your percentage error exceeds your calculated uncertainty, there is a systematic error — this is worth discussing in Evaluation.

How to Get Criterion-Level Feedback on Your Chemistry IA

The difference between a Chemistry IA that scores 18/24 and one that scores 22/24 is usually two specific marks on Evaluation and one on Analysis. These are not visible from a general read-through — they require mapping each paragraph against the criterion descriptors to identify exactly what is missing.

IBLens analyses your IB IA or essay against the official marking criteria and shows you precisely where marks are being lost — before your teacher submits your moderated grade.

Upload your Chemistry IA draft to IBLens for rubric-based feedback →