IB Chemistry IA guide: how to plan, write and score well (2025 criteria)

What is the IB Chemistry IA?

The Chemistry internal assessment (IA) is a single scientific investigation. You choose a question, gather or select quantitative data to answer it, analyse that data, and write a report of at most 3,000 words. Your teacher marks it and the IB moderates a sample. It is worth 20% of the final grade, and the task and criteria are the same at SL and HL.

The guide recommends about 10 hours of teaching time for the whole task. Most students also spend time outside lessons on data collection and writing, so plan for it. The question does not need to go beyond the syllabus, and it does not need to be new to science. It needs to be yours, measurable, and big enough that every criterion has something to mark.

The rules that trip students up

• Your raw data are yours. You may not hand in the same data set as a classmate.
• Groups are allowed, but reports are not shared. Up to three students may work together, but each one must vary a different independent variable, measure a different dependent variable, or use different data. Each writes an individual report.
• One draft. Your teacher can read and advise on one draft, orally or in writing, but does not edit it. The next version is the final one.
• Reference everything. Missing or improper referencing counts as academic malpractice.
• No reuse. The same piece of work cannot count for both the IA and the Extended Essay.

What changed from the pre-2025 criteria

If a sibling, a website or an old exemplar mentions Personal engagement or Communication, it describes the previous model. The 2025 course has four equal criteria, one clear word limit, and no separate marks for presentation or personal engagement. Old exemplars still show good chemistry and good data handling, but they were marked on a different scheme, so do not copy their structure blindly.

The shift matters because the marks that used to reward effort and presentation now reward reasoning: explained choices, carried-through uncertainty, and a conclusion that stays inside the numbers. The rest of this guide is built around those four habits.

How the Chemistry IA is marked: the four criteria

Each criterion is worth 6 marks and is marked in three bands: 1 to 2, 3 to 4 and 5 to 6. Your teacher gives whole numbers, choosing the band that fits best. A top mark does not need a perfect report. It needs a few things done clearly and cleanly.

The bands are separated by command terms. Describe means say what you did. Explain means say what you did and why. A long, detailed description of your method is still a description. Look for the word “because” in the right places.

Criterion A: Research design (6 marks)

What it rewards. A research question set in context, thoughtful choices about how you collect or select data, and a method that someone else could repeat.

What top work does. The question names the independent and dependent variables, describes the system, and comes with background theory that bears directly on it. Each choice (range, interval, number of repeats, instrument, control) comes with a reason. The method is clear enough to repeat without guessing.

Where marks go. A topic instead of a question; three or four values over a narrow range; controls that are named but not measured; concentrations copied from a worksheet with no reason; a page of textbook background that never reaches a prediction; safety that is generic.

Our tip. Next to every number in your method (volume, mass, interval, repeats), write “because” and finish the sentence. If you cannot, the choice is not yet explained.

Criterion B: Data analysis (6 marks)

What it rewards. Data that are recorded, processed and presented so that the reader can follow them (clear) and so that the conventions are right (precise): units, decimal places, significant figures, labelled tables and graphs. Uncertainties must be handled properly.

What top work does. Raw tables carry units and uncertainties. Each type of calculation has a worked example. Uncertainties travel from the raw data to the final result and onto the graph. The graph suits the chemistry, and any gradient or intercept comes with its own uncertainty.

Where marks go. An uncertainty found once (for the burette, say) and never used again; a rough titre inside the mean; graphs without error bars or with points joined dot to dot; raw data to two significant figures and results to six; a percentage difference from the literature passed off as the uncertainty.

Our tip. Pick one processed number in your report and trace it back to the raw data. If any step is missing, the reader cannot follow it either.

Criterion C: Conclusion (6 marks)

What it rewards. How well you answer your question in line with your analysis (including its uncertainties) and with accepted chemistry.

What top work does. The first sentence answers the question using both variables and numbers. The claim is justified by the processed data and their uncertainties, limited to the range you measured, and compared with a cited value whose source and conditions the reader can trace.

Where marks go. Retelling the method; claiming a trend that the error bars would allow to be flat; “close to the literature value” with no number or source; a value for a different temperature or system; the word “proves”.

Our tip. Underline the variables in your question, then check that your first conclusion sentence contains both.

Criterion D: Evaluation (6 marks)

What it rewards. An honest look at your method: specific weaknesses and limitations, how much each one matters, and realistic improvements.

What top work does. Every weakness belongs to this method and can be seen in this data (scatter, an offset, a curve, an outlier, a large error bar). The weaknesses are weighed against each other, ideally with numbers. Each improvement says what changes in the procedure and what you would expect to see.

Where marks go. “Human error” and “faulty equipment” with no sign they affected your results; ten weaknesses and no ranking; “use more accurate equipment” with no instrument named.

Our tip. For each weakness, ask: where in my data could a stranger see this? If the answer is nowhere, cut it or go and look harder.

Three habits separate the bands in all four criteria: give reasons, not lists; give numbers, not adjectives (a stated uncertainty beside a stated difference); and link the criteria, so that a prediction in A is tested in C, an uncertainty from B is used in C, and a weakness in D shows in the graph in B.

How to choose a Chemistry IA topic

A good topic is less about how impressive it sounds and more about whether it can produce clean, repeatable numbers. The investigation must have enough extent and depth for every criterion to be marked, so a task that gives no trend, no gradient and nothing to criticise is too small. Use these seven tests.

1. Clear variables. You can name what you will change and what you will measure, and your school equipment can measure it to a useful precision.
3. Range and repeats. You can vary the independent variable smoothly over a wide range, at least five values in our experience, and you can afford repeats in the time you have.
4. Theory to test against. A known relationship gives you a prediction in Criterion A, a comparison in C and assumptions to examine in D.
5. Feasible and safe. It fits your time, equipment and safety rules. A pilot run in the first fortnight saves weeks.
7. Yours. Not the standard school practical with the same variables and ranges, which every classmate will also pick.
8. Room for uncertainty work. If one measurement dominates the error, you can analyse it. If every value is exact and nothing varies, there is little to say.
10. Honest scope. Small and clean beats broad and thin.

Topics that tend to fail: anything that cannot give five measured values; a yes-or-no question; a method needing equipment you cannot reach; work that harms people or animals; a measurement you cannot repeat; and a question already answered by a standard practical with the same variables.

Start from the technique you can do reliably, then ask what you could vary over a wide range and what theory says should happen. Our full list of ideas is in 87 IB Chemistry IA ideas and EE topics, and the free 25 Chemistry IA topic guides each come with a set-up diagram and a sample graph. Treat ideas as starting points. The final question, plan and data must be your own.

How to write a Chemistry IA research question

The IB describes a question “with context” as one that names the dependent and independent variables (or two correlated variables), briefly describes the system, and has background theory of direct relevance. In practice that means four parts, shown below.

Weak, better, strong

These three examples are our own, written to show the difference. They are not topics to copy.

What changed each time? The strong versions give a range with units, name a measurement route for the dependent variable, fix the system and conditions, and point to a relationship (Arrhenius, first-order decay, a predicted potential change) that you can compare your result with in Criterion C. They are also longer, and that is fine. A question can be a sentence and a half if every part earns its place.

The 5-point check

Before you commit, score your question against five points. This is the same check our Coach applies to a research question: focus, variables, feasibility, data, and fit with the criteria.

Variables, controls and how much data you need

Every Chemistry IA has three kinds of variable. The independent variable is what you change. The dependent variable is what you measure. Control variables are everything else that could change the result. A control is only a control if you say how you hold it constant and how you would notice if it drifted.

Take the thiosulfate example from Figure 4's family of questions. The table shows what a good plan writes down.

Why five values and three repeats

The IB does not give a magic number, and neither is five values with three repeats an IB rule. It is our working minimum, and the reason is simple. Five values spread over a useful range show the shape of the trend: a straight line, a curve, or a plateau. Three values cannot. Three repeats at each value show the scatter, so you can tell a real trend from noise and justify the uncertainty you quote.

Notice what the range row tells you. At 20 °C the three times differ by 7 s, which is wide, so that mean is less secure. At 40 to 60 °C the spread is 2 s, but 2 s on an 11 s time is still about 18%. That is useful to know before the evaluation, because it says where to add repeats or use a better way of timing.

Choose the range so the trend is clear at both ends, use even steps so a graph is easy to read, and add repeats where the scatter is largest. Explain each choice in Criterion A.

Methods that give clean data

Every technique has one classic trap that quietly costs marks. Here are the five we see most, each with a fix you can build into the plan.

Titration

The trap. Averaging every titre, including the rough one and any overshoot, and counting the burette uncertainty once.

The fix. Do a rough titre to find the neighbourhood, then repeat until you have concordant titres (a spread of 0.10 cm³ is a common school convention; ask your teacher what yours is). Average only those, say how many you used, and give a reason for each result you leave out. Describe the standard solution fully (mass weighed, flask size, dilution steps) and explain your indicator and how you recognise the end-point.

Calorimetry

The trap. Reading the highest temperature and calling the difference ΔT. A cup loses heat while it mixes, so the true rise is bigger than the one you see.

The fix. Record the temperature of the reactants for a few minutes, mix at a known time, then record every 30 seconds afterwards. Extend the cooling line back to the moment of mixing and read ΔT from there, as in Figure 7. Use a lid and an insulated cup, and remember that ΔT is a difference, so it uses two thermometer readings. A thermometer good to ±0.5 °C gives ±1.0 °C on 8.4 °C, which is 12%. A probe good to ±0.1 °C gives ±0.2 °C, which is 2.4%.

Colorimetry

The trap. Reading an unknown that lies outside the calibration, or using a wavelength and cuvette handling that change from reading to reading.

The fix. Make at least five standards across the range you expect, zero the instrument with a blank, choose the wavelength where your coloured species absorbs most strongly, and place the cuvette the same way each time. Keep the unknowns inside the calibrated range, where the line is straight.

Rates of reaction

The trap. Letting the temperature drift, starting the timer late, and hand-timing something so fast that human reaction time is a big share of the reading.

The fix. Pre-warm reagents in the bath, record the temperature at the start and the end of each run, and write one fixed start procedure. Choose concentrations that give times of a few tens of seconds or more, or use a sensor, a gas syringe or a data logger. For “initial rate”, take the slope at the start, not an average over the whole reaction.

Electrochemistry

The trap. Recording a potential that is still drifting, or, in electrolysis, losing deposit when you handle and weigh the electrode.

The fix. For cells, let the reading settle and record it at a fixed time, with fresh electrode surfaces and a fresh salt bridge. For electrolysis, hold the current steady, rinse and dry the electrode gently, and dry to constant mass before weighing.

Uncertainties in the Chemistry IA, with a worked example

Uncertainty is the single biggest reason Data analysis marks stop at 4. The idea is simple. Every measurement is a range, not a point. You record it as a ± value, carry it through each calculation, show it on the graph, and use it to interpret your result.

The Chemistry guide asks you to record uncertainty as a ± range, to propagate it through addition, subtraction, multiplication and division (and, at HL, powers), to express it as absolute, fractional or percentage uncertainty to a sensible number of significant figures, and to use R² to judge how well a line or curve fits.

Instrument uncertainties

These are typical school values. Justify the ones you use, and prefer the tolerance printed on your own glassware or in the maker’s manual. If your repeats spread more than the instrument suggests, use the spread.

Propagation rules

A worked example: acidity of vinegar

Here is a full calculation with invented numbers. A student dilutes 25.00 cm³ of vinegar to 250.0 cm³ and titrates 25.00 cm³ aliquots against 0.1000 mol dm⁻³ sodium hydroxide, assumed standardised with an uncertainty of ±0.0005 mol dm⁻³. The concordant titres are 21.15, 21.20 and 21.25 cm³ (Figure 6). The reaction is CH₃COOH + NaOH → CH₃COONa + H₂O, one to one.

1. Mean titre. (21.15 + 21.20 + 21.25) ÷ 3 = 21.20 cm³. The spread gives ±0.05 cm³, but the two burette readings give ±0.10 cm³, which is bigger, so use ±0.10 cm³. That is 0.10 ÷ 21.20 = 0.47%.
3. Moles of NaOH. 0.1000 × 21.20 ÷ 1000 = 2.120 × 10⁻³ mol, equal to the moles of acid in the aliquot.
4. Diluted acid. 2.120 × 10⁻³ ÷ 0.02500 dm³ = 0.08480 mol dm⁻³.
5. Undo the dilution. The factor is 250.0 ÷ 25.00 = 10, so the vinegar is 0.8480 mol dm⁻³.
6. Percentage uncertainties. Titre 0.47%; NaOH 0.0005 ÷ 0.1000 = 0.50%; aliquot pipette 0.06 ÷ 25.00 = 0.24%; vinegar pipette 0.06 ÷ 25.00 = 0.24%; flask 0.15 ÷ 250.0 = 0.06%.
8. Add them (the result is a product and quotient of these): 0.47 + 0.50 + 0.24 + 0.24 + 0.06 = 1.51%.
9. Back to absolute. 0.8480 × 0.0151 = 0.0128, so ±0.013 mol dm⁻³.

Result: 0.848 ± 0.013 mol dm⁻³ (1.5%), with the result rounded to the place of its uncertainty. As a mass concentration, with M = 60.05 g mol⁻¹, that is 50.9 ± 0.8 g dm⁻³.

Figure 9 is the bridge to your evaluation. It tells you which uncertainty matters, with numbers, instead of leaving you to guess. It also shows what the sum does not include. Uncertainties of this kind describe random scatter and the resolution of instruments. A systematic error, such as an end-point that is always read a drop late, is not in the 1.51% and belongs in the evaluation.

Uncertainty on a graph

When uncertainty is significant, show it as error bars. To find the uncertainty in a gradient or intercept you can use your spreadsheet’s regression statistics, or draw the steepest and shallowest straight lines that still pass through all the error bars and take half the difference. The Chemistry guide lists R² rather than maximum and minimum gradient lines, so check which method your teacher prefers, and state the one you used.

The best-fit intercept in Figure 10 is 0.003, and the two extreme lines cross the axis at about −0.014 and +0.036. That band includes zero, which is consistent with a calibration that should pass through the origin. Without error bars, you could not say that.

Data analysis: from raw data to a graph

Good data analysis is mostly good housekeeping, done the same way every time.

Table conventions

• Put the quantity and its unit in the column heading, not in each cell: t / s, V / cm³.
• Give each measured value to the same number of decimal places as the instrument allows, and give the uncertainty with the same decimal places as the value.
• Number every table and graph, refer to it in the text, and keep qualitative observations (a colour change, a smell, a doubtful end-point) beside the numbers they explain.
• Round processed values to the number of significant figures your data justify, not to what the spreadsheet displays. Round the final result to the place of its uncertainty.

One sample calculation, done properly

Show one worked example for each type of calculation. Using the 20 °C column of Figure 5: the mean time is 141.7 s, and the three values differ by 7 s, so half the range is ±3.5 s, or 2.5% (3.5 ÷ 141.7). Taking rate as proportional to 1/t: 1 ÷ 141.7 = 0.00706 s⁻¹. A reciprocal keeps the same percentage uncertainty, 2.5%, so the absolute uncertainty is 0.00706 × 0.025 = 0.00018, quoted as ±0.0002 s⁻¹. The processed value is 0.0071 ± 0.0002 s⁻¹.

Choosing and linearising graphs

Plot the graph that answers your question, and choose a curve only if the chemistry supports it. A polynomial fitted only because it matches the points is hard to defend. Where theory gives a straight line after a transformation, plot that, because a gradient and an intercept carry meaning.

The Arrhenius plot is the classic linearisation. Suppose a first-order reaction gave these illustrative values of the rate constant k:

If k is known to about ±5%, each ln k is good to ±0.05. The steepest and shallowest lines through those error bars give gradients of about −6420 K and −5930 K, so the activation energy is about 51 ± 2 kJ mol⁻¹.

R² is not an uncertainty

R² here is 0.998, and it is tempting to write “very reliable” beside it. Do not. R² tells you how closely the points follow the line. It says nothing about how far the gradient, and so E_a, could be from the truth. A curved set of points can still give R² above 0.99. Quote R², look at the pattern of the residuals and the error bars, and take your gradient uncertainty from the lines or the regression statistics.

How to write the conclusion

A conclusion earns its marks by being specific, honest and traceable.

Answer with numbers and uncertainty

Start with a sentence that contains both variables and a result: what happened to the dependent variable as the independent variable changed over your range, with the key value and its uncertainty. Then say whether the trend is larger than your uncertainties allow. If your error bars would let a flat line through the data, you cannot claim a trend. Use the language of evidence (“supports”, “is consistent with”), keep to the range you measured, and say whether your prediction from Criterion A held.

Give the literature value, with its source and conditions

Quote an accepted value or relationship, the conditions it applies to (temperature, concentration, pressure), and a reference a reader could find: author or publisher, title, edition or database, and the table or page. “Google” or a bare website name will not do. A value for 298 K does not test a result measured at 305 K.

Percentage error against percentage uncertainty

These are different things, and the conclusion should read them together. The percentage error measures how far you are from theory. The percentage uncertainty measures what your own measurements can justify. Figure 12 shows how to decide what to say.

Two cautions. A tiny percentage error is not proof of accuracy, because a systematic error can hide inside your uncertainty. And if your error is much smaller than your stated uncertainty, check whether you have overestimated the uncertainty before you celebrate.

Explain the chemistry

Close the loop with particle-level reasoning that fits your data: for a rate experiment, collisions with enough energy; for an enthalpy value, the bonds broken and formed. Keep the explanation tied to what you measured. If the data cannot show a cause, say so and describe the limit rather than overreaching.