The most damaging IB Chemistry IA common mistakes usually begin before data collection: a vague research question, an impractical method, insufficient repeated trials, weak treatment of uncertainty, or an evaluation filled with generic comments. These problems are connected. A poorly focused question produces an unfocused method, weak data, and a conclusion that is difficult to justify.
Under the Chemistry course first assessed in 2025, the IA is officially called the scientific investigation. It contributes 20% of the final SL or HL grade, has a maximum report length of 3,000 words, and is assessed using four criteria worth six marks each: Research design, Data analysis, Conclusion, and Evaluation. This article explains how to identify the most frequent errors and replace them with specific, scientifically defensible choices.
The most common mistakes at a glance
Common mistake
Why it weakens the investigation
Concrete fix
Vague research question
Variables and scope are unclear
Name the independent variable, dependent variable, chemical system, range, and relevant conditions
Unsafe or unfeasible method
Reliable data cannot be collected within school constraints
Pilot the procedure, complete a risk assessment, and simplify the design where necessary
Too few trials or data points
Variation and reliability cannot be judged
Use several independent-variable levels and enough repeats to estimate variability
Missing uncertainties
Precision and confidence in the trend remain unclear
Record instrumental uncertainties, propagate them where relevant, and interpret error bars or spread
Superficial processing
Calculations do not answer the research question
Select processing that reveals the chemical relationship, such as rate, equilibrium constant, or calibration analysis
Unsupported conclusion
Claims go beyond the evidence
Quote processed results, discuss uncertainty, and compare with accepted scientific context
Generic evaluation
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Limitations are listed without showing their effects
Explain the direction, magnitude, and consequence of each important weakness, then propose a matched improvement
Mistake 1: Writing a vague research question
A question such as “How does concentration affect reaction rate?” is measurable, but it is not sufficiently contextualized. It does not identify the reactants, concentration range, dependent measurement, temperature, or method used to determine rate.
A stronger version would be:
How does changing the concentration of hydrochloric acid from 0.20 to 1.00 mol dm⁻³ affect the initial rate of its reaction with magnesium ribbon at 298 K, determined from hydrogen gas volume collected during the first 30 seconds?
This version establishes the independent variable, dependent variable, chemical system, range, important controlled condition, and measurement technique. The context should also explain why the chosen relationship is chemically meaningful, perhaps through collision theory or an expected rate law.
Before approving the question, check that it:
investigates a genuinely chemical relationship
produces quantitative data capable of answering the question
has a manageable independent-variable range
can be completed safely with available apparatus
allows meaningful analysis beyond describing an observation
Mistake 2: Choosing an unsafe or unfeasible method
An ambitious experiment is not automatically a strong investigation. Methods involving concentrated corrosive reagents, uncontrolled heating, toxic gases, high pressure, or unavailable analytical equipment may be impossible to conduct safely or consistently.
Complete a pilot investigation before committing to the final design. A pilot can reveal that a reaction is too fast to time manually, a colour change is too subjective, the measured change is smaller than the instrument resolution, or the proposed concentration range produces no detectable trend.
A workable method should specify:
reagent concentrations and volumes
apparatus capacities and precision
how each control variable will be maintained
how and when measurements will be taken
relevant hazards, precautions, and waste disposal
enough procedural detail for another student to reproduce the investigation
If a reaction is too fast for manual timing, reduce the concentration or use a sensor that records continuously. If a gravimetric change is smaller than the balance uncertainty, increase the measurable quantity or choose a more sensitive technique. Safety should shape the design rather than appear as a disconnected table added after the experiment.
Mistake 3: Collecting too few trials or values
The IB does not prescribe a universal minimum number of trials or independent-variable levels. Claims that every Chemistry IA must contain exactly five values and five repeats are recommendations, not official rules.
Nevertheless, one measurement at each condition is rarely enough to establish reliability. Repeated measurements reveal random variation, allow means and measures of spread to be calculated, and help distinguish an anomalous result from a genuine pattern.
For many controlled laboratory investigations, five or more independent-variable levels with three to five repeats per level is a sensible planning starting point. The appropriate design depends on the reaction, variability, available time, and type of analysis. A pilot should determine whether more repeats or a narrower spacing of values is needed.
Repeats must be genuine replicates. Reading the same burette value several times is not equivalent to preparing and running the reaction again. Students needing help selecting suitable processing can consult RevisionDojo’s explanation of statistical analysis in a science IA.
Mistake 4: Omitting or mishandling uncertainties
Writing an apparatus list with tolerances is not enough. Uncertainty must be carried from data recording into processing and then used to judge the strength of the conclusion.
Raw-data tables should normally place the unit and absolute uncertainty in the heading, such as Volume of H₂ / cm³ ± 0.5 cm³. Recorded decimal places should match the measuring instrument. Processed quantities may require propagated uncertainty, depending on how the raw measurements are combined.
Students should distinguish between:
instrumental uncertainty, associated with measurement resolution or manufacturer information
random variation, shown by the spread among repeated trials
systematic error, which shifts results consistently and is not removed by averaging
Error bars must represent a stated quantity, such as propagated uncertainty or standard deviation. Do not add error bars merely for appearance. Explain whether they are large compared with the observed differences, whether adjacent values overlap, and how this affects confidence in the proposed relationship.
RevisionDojo’s Chemistry IA data analysis guide explains data presentation, processing, graphs, and uncertainty treatment in more detail.
Mistake 5: Processing data without answering the question
A graph and an R² value do not automatically constitute strong analysis. Processing must be chemically and mathematically appropriate for the stated research question.
For example, total reaction time may be less informative than initial rate. Absorbance values may need a calibration curve before concentration can be determined. An Arrhenius investigation normally requires a justified transformation such as plotting ln k against 1/T, not simply drawing a straight trend through temperature and time.
Show at least one clearly labelled sample calculation and apply consistent units, significant figures, and decimal places. Address anomalous points transparently rather than deleting them because they reduce the correlation coefficient. A strong analysis explains why the chosen model is suitable and identifies where the data depart from it.
Mistake 6: Writing an unsupported conclusion
A conclusion should directly answer the research question using processed evidence. Statements such as “the hypothesis was correct” or “rate increased with concentration” are too limited unless they are supported quantitatively.
A stronger conclusion might state the size and direction of the change, quote a gradient or calculated constant with units and uncertainty, and explain whether the pattern agrees with the expected chemical model. It should then compare the result with accepted scientific context, such as an established theoretical relationship or a credible literature value.
If the uncertainty is large, the conclusion must acknowledge that limitation. Avoid claiming causation, reaction order, or exact agreement when the data only show a broad association. Comparing experimental and literature values is useful only when conditions and definitions are sufficiently similar.
Mistake 7: Producing a generic evaluation
“Use better equipment,” “avoid human error,” and “do more trials” are not developed evaluations. They fail to identify the precise methodological weakness, its effect on the data, or how the proposed change would improve the investigation.
Use a structured weakness-to-improvement chain:
Specific weakness
Effect on results
Matched improvement
Manual timing begins after some gas has escaped
Early gas volume is underestimated, particularly for faster trials
Use a sealed pressure sensor with automatic data logging
Temperature rises during an exothermic reaction
Rate changes during the trial and temperature is not truly controlled
Use a thermostatically controlled water bath and monitor the reaction mixture
Volumetric cylinder is used to prepare solutions
Concentration uncertainty is unnecessarily large
Prepare solutions with volumetric flasks and calibrated pipettes
Magnesium ribbon has inconsistent oxide coating
Effective surface condition varies between trials
Clean equal lengths using a standardized procedure immediately before use
Prioritize weaknesses by their likely impact rather than listing every minor imperfection. Explain whether each one causes random variation, systematic bias, or restricted validity. The RevisionDojo guide to writing an IA evaluation and a carefully chosen Chemistry IA exemplar can help students recognize this level of specificity.
Final quality-control checklist
Before submission, confirm that:
the research question and method investigate the same variables
the method is safe, feasible, controlled, and reproducible
the data quantity is justified rather than based on an invented IB minimum
tables contain units, uncertainties, and consistent precision
calculations and graphs genuinely address the research question
uncertainty is interpreted, not merely displayed
the conclusion uses numerical evidence and accepted chemistry
each evaluation point links a weakness, effect, and realistic improvement
all external ideas, data, and literature values are cited
the report remains within the official 3,000-word maximum
RevisionDojo’s IB Chemistry IA Grader can provide criterion-based feedback while a draft is still being revised. Jojo AI can help clarify unfamiliar chemistry or identify unclear reasoning, but the decisions, calculations, analysis, and final writing must remain the student’s own work.
Conclusion
The most important fixes happen early: narrow the research question, pilot the method, collect enough repeated data, and plan uncertainty treatment before beginning the final experiment. During writing, make every calculation serve the question, support the conclusion numerically, and replace generic evaluation comments with specific cause-and-effect reasoning.
Students who want a final structured check can use RevisionDojo’s Chemistry IA guide, exemplar library, and IA Grader. These tools are most useful when they support careful revision of the student’s own scientific thinking rather than replace it.
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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