IB Biology IA Examples to Get You Started
What Is the IB Biology Internal Assessment?
The IB Biology Internal Assessment (IA) is an individually designed scientific investigation that forms 20% of a student’s final IB Biology grade. You choose your own research question, design and carry out your own experiment, collect and analyse your data, and write up your findings in a report of up to 3,000 words. It is completed during your two years of IB study, marked by your teacher, and then moderated by an external IB examiner.
Unlike a standard laboratory practical, the IB Biology IA has no predetermined outcome and no guided procedure to follow. The research question, the experimental design, and the analytical approach are all yours to determine.
The IA is assessed against four criteria under the current IB Biology syllabus:
Research Design
Data Analysis
Conclusion
Evaluation
Understanding what each criterion demands before you begin is essential because a strong IA is not simply one that produces clean, interesting data. Rather, it is one that demonstrates scientific thinking at every stage, from the precision of the research question to the depth of the final evaluation.
One of the most effective ways to develop that understanding is to study examples. Three IB Biology IA examples follow, spanning ecology, biochemistry, and genetics, with detailed commentary on what makes each research question work, what strong analysis actually looks like, and the specific mistakes that cost students marks.
What Makes a Strong IB Biology IA Research Question?
The research question is the foundation of the entire investigation. A poorly scoped question produces weak data, limited analysis, and an evaluation with little substance to reflect on. Getting it right before you design a single aspect of your methodology is the single most important step in the IA process.
Every strong IB Biology IA research question demonstrates three qualities:
Specificity: both the independent and dependent variables are named explicitly, with units and ranges defined.
Biological relevance: the question connects clearly to a specific area of the IB Biology syllabus.
Measurability: the dependent variable is quantifiable using equipment available in a school laboratory.
The difference between a weak and a strong research question is best illustrated directly.
Weak: ‘How does temperature affect enzyme activity?’
Strong: ‘How does increasing the temperature of the substrate (10, 20, 30, 40, and 50°C) affect the rate of hydrolysis of starch by amylase, as measured by the time taken for the iodine solution to lose its blue-black colour?’
The weak version is too broad to generate meaningful data and could apply to dozens of different experiments. It names neither variable with any precision, specifies no range, and provides no indication of how the dependent variable will be measured. The strong version does all of that within the question itself, and crucially, it specifies the measurement method rather than simply naming the dependent variable. Stating that enzyme activity will be measured is not the same as explaining how it will be measured. That distinction is precisely what the Research Design criterion rewards.
IB Biology IA Examples across Different Topic Areas
Each of the three IA examples below covers a distinct area of the IB Biology syllabus and follows the same analytical framework: the research question, why it works, the experimental approach, what strong analysis looks like, and the most instructive mistake students make in that type of investigation.
IB Biology IA Example 1: How Light Intensity Affects the Rate of Photosynthesis
Research Question
How does light intensity (measured in lux at distances of 5, 10, 15, 20, and 25 cm from a light source) affect the rate of photosynthesis in Elodea, as measured by the number of oxygen bubbles produced per minute?
Why This Research Question Works
The independent variable (light intensity, operationalised through distance from the light source) is defined with a clear numerical range. The dependent variable (oxygen bubble count) is identified with a specific measurement window. The organism is named rather than described generically. Each of these details signals methodological awareness before the experimental design has even been described.
What the Experimental Approach Looks Like
An Elodea sprig is submerged in a sodium hydrogen carbonate solution to ensure a constant supply of carbon dioxide, and positioned at each of the five distances from a lamp. The number of oxygen bubbles produced per minute is counted across three repeated trials at each distance, and the mean value is recorded.
What Strong Analysis Looks Like
A strong analysis plots mean bubble count against light intensity on a line graph with error bars, applies a curve of best fit rather than straight-line interpolation, and connects the observed trend to the light-dependent reactions of photosynthesis. Crucially, it explains why bubble production increases with light intensity at the molecular level, citing the role of photons in exciting chlorophyll electrons and driving ATP and NADPH production in the thylakoid membrane.
Common Mistakes Students Make
Students frequently count bubbles without accounting for variation in bubble size across trials, which introduces a significant random error into the dependent variable. A stronger methodology collects the gas produced in a capillary tube and measures its volume directly, converting a qualitative count into a quantitative measurement. This single methodological adjustment substantially strengthens the Data Analysis criterion by producing genuinely comparable data across trials.
IB Biology IA Example 2: How pH Affects Enzyme Activity
Research Question
How does the pH of the buffer solution (pH 4, 5, 6, 7, 8, 9, and 10) affect the rate of breakdown of hydrogen peroxide by catalase extracted from potato tissue, as measured by the height of foam produced in millimetres after 60 seconds?
Why This Research Question Works
This research question names the enzyme source (potato tissue) and the substrate (hydrogen peroxide) explicitly, rather than referring vaguely to ‘an enzyme’ and ‘its substrate’. It defines the pH range across seven data points, which is sufficient to identify a clear optimum and observe the behaviour of the enzyme on either side of it. The measurement method for the dependent variable is quantitative and reproducible.
What the Experimental Approach Looks Like
Catalase is extracted from potato tissue and added to hydrogen peroxide solutions buffered at each target pH. Foam height is measured in millimetres after a fixed 60-second interval across three repeated trials at each pH level. Buffer solutions are prepared in advance to ensure that pH values remain stable throughout each trial.
What Strong Analysis Looks Like
A strong analysis identifies the optimum pH from the data and connects this to the concept of active site geometry, explaining that the enzyme's tertiary structure is maintained by interactions including hydrogen bonds and ionic bonds that are sensitive to changes in hydrogen ion concentration. It discusses the significance of the error bars at each data point in terms of what they reveal about experimental reproducibility, and comments on whether the spread of results is consistent across the pH range or concentrated at particular values.
Common Mistakes Students Make
The most persistent error in this type of investigation is describing the trend in foam height rather than explaining it. Noting that foam height decreased at extreme pH values is an observation. Explaining that acidic and alkaline conditions disrupt the hydrogen and ionic bonds maintaining the enzyme's tertiary structure, altering the geometry of the active site and reducing its complementarity to the substrate, is analysis. That distinction is precisely what separates a mid-range Data Analysis mark from a high one.
IB Biology IA Example 3: Variation Within a Species
Research Question
Is there a statistically significant difference in the leaf area of Hedera helix collected from sun-exposed and shade-exposed positions on the same plant?
Why This Research Question Works
What distinguishes this research question from the previous two is its use of a statistical hypothesis framework. Rather than asking how one variable affects another, it asks whether an observed difference is statistically significant, requiring the student to engage with inferential statistics and the concept of the null hypothesis. This design connects directly to the IB Biology syllabus topics of variation and adaptation, and gives the investigation a clear theoretical prediction to test against the data.
What the Experimental Approach Looks Like
A minimum of 30 leaves are collected from sun-exposed positions and 30 from shade-exposed positions on the same plant, controlling for genetic variation across the two sample groups. Leaf area is calculated by tracing each leaf onto squared paper and counting the enclosed squares. A Student's t-test or Mann-Whitney U test is then applied to determine whether the difference in mean leaf area between the two groups is statistically significant.
What Strong Analysis Looks Like
A strong analysis does not simply report whether the result was statistically significant. It interprets what that significance means biologically, connecting the observed morphological differences to the adaptive advantages of larger leaf surface area in low-light conditions, where maximising light capture per unit of chlorophyll is a significant selective pressure. It also comments honestly on the limitations of using a single plant as the source of both sample groups, acknowledging that this controls for genetic variation but limits the generalisability of the findings.
Common Mistakes Students Make
Students frequently apply a statistical test without demonstrating that they understand what it is measuring. Stating that ‘the p-value was less than 0.05, therefore the result is significant’ is insufficient on its own. A strong write-up explains that a p-value below 0.05 indicates less than a 5% probability that the observed difference arose by chance, and therefore supports rejection of the null hypothesis, before connecting that statistical conclusion back to the original biological question.
The Lessons These Biology IA Examples Teach Us
Studying individual examples is valuable, but the deeper benefit comes from recognising the patterns that run across all of them. The same distinctions that separate a strong ecology investigation from a weak one apply equally to biochemistry and genetics. Understanding those patterns is what allows you to apply them to your own work, regardless of the topic you choose.
Specificity in the research question is non-negotiable. Every strong research question above names both variables with units, ranges, and a defined measurement method. This is not a stylistic preference. It is a direct signal to the examiner that the student has thought carefully about experimental design from the outset. If your own research question could apply to five different experiments, it needs narrowing before you design a single aspect of your methodology.
Biological reasoning is what the Data Analysis criterion rewards, not data volume. Across all three examples, the distinction between a mid-range and a high-scoring analysis comes down to whether the student explains the biology behind the trend or simply describes it. No volume of well-presented data compensates for the absence of biological reasoning.
Strong evaluations are specific, not apologetic. The weak evaluation statements in the examples above share a common flaw: they acknowledge that something went wrong without explaining what, why, or how significantly. A strong evaluation identifies the specific source of error, explains its likely direction and magnitude, and proposes a realistic, equipment-specific improvement. Vague references to human error are not evaluations.
The gap between a grade 4 and a grade 7 appears in the depth of biological reasoning, not the quality of the data. Students who produce clean, well-organised data but explain it superficially consistently underperform relative to their ability. The IB Biology IA does not reward perfect results. It rewards the ability to think and write like a scientist, connecting experimental observations to biological theory with precision and confidence.
Treat these IA examples as a thinking framework rather than a set of topics to replicate. The most compelling IAs are those where the student's genuine curiosity about the research question is visible throughout the work.
Get Expert Guidance on Your IB Biology IA with BartyED
Understanding what a strong IB Biology IA looks like in theory and producing one independently are two different challenges. The research question, experimental design, data analysis, and evaluation each demand a distinct set of skills, and a weakness in any one of them affects the overall mark.
BartyED's IB Biology tutors work one-to-one with students in Hong Kong and online, supporting every stage of the IA process. Whether you are at the beginning of the process or working to strengthen a draft that is not yet where it needs to be, a BartyED IB Biology tutor provides the individual attention and subject expertise that makes a measurable difference to outcomes.
Get in touch by phone +852 2882 1017, WhatsApp (+852 57215837), email (enquiries@bartyed.com), or via the form below to find out how a BartyED IB Biology tutor can support your internal assessment.
Frequently Asked Questions
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The IB Biology IA is worth 20% of a student’s final grade.
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It is recommended that students pick a topic that reflects an area of interest in the subject. So, start by thinking about what it is you find inspiring and build out from there.
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The key to a good Biology IA is a strong research question and giving yourself enough time to develop multiple drafts. Rushing the writing process is not recommended.
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The IB Biology IA should be no longer than 3,000 words.