In short
A strong IB Biology IA idea has two measurable variables, a specific system and enough quantitative data to meet the four 2025 criteria: Research design, Data analysis, Conclusion and Evaluation. Below are 12 school-lab, fieldwork, database and model ideas with research questions, variables, methods and statistics, none of which harms animals.
What the 2025 IA needs from an idea
A good IB Biology IA idea is a question you can answer with quantitative data from a school lab, fieldwork, a spreadsheet model, a database or a simulation, in about 10 hours of class time. The IA is worth 20% of your grade at SL and HL, and it is marked out of 24, with 6 marks for each of four criteria.
| Criterion | What the report must show | What to look for in an idea |
|---|---|---|
| Research design | A research question described in a specific context, with explained choices (measurement, range, repeats, controls, safety and ethics) and a method that others could repeat. | Two measurable variables and a system you can name. |
| Data analysis | Data recorded, processed and presented clearly and precisely, with appropriate uncertainties. | A measurement where you can record ± uncertainties and process the data further. |
| Conclusion | An answer to the research question that is justified, fully consistent with the analysis and compared with accepted science. | A topic where published values or theory exist to compare with. |
| Evaluation | Specific weaknesses and limitations, their relative impact, and realistic improvements. | A method with real sources of error that you can discuss in detail. |
Our IB Biology IA guide has the full band descriptors, and the SL version is identical because the IA is the same at both levels.
Twelve IA ideas by theme
Every idea assumes a standard school lab or, for the database and model ideas, a computer with a spreadsheet. The ranges are starting points. Run a short pilot and change them if the data are too flat or too steep.
Molecules: enzymes, respiration and photosynthesis
1. Catalase and hydrogen peroxide concentration (C1.1, see enzymes and metabolism)
Research question: What is the effect of hydrogen peroxide concentration (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 %) on the initial rate of oxygen production by catalase in a yeast suspension, measured with a gas syringe over 60 s?
- Independent: hydrogen peroxide concentration, diluted from a 3 % stock.
- Dependent: volume of oxygen collected, converted to an initial rate (cm³ s⁻¹).
- Controlled: yeast mass and suspension volume, temperature (water bath), pH (buffer) and reaction time.
- Method: Mix fixed volumes of yeast suspension and peroxide, stopper quickly and read the syringe every 10 s. Do five repeats per concentration. Wear eye protection.
- Statistics: Initial rate from the gradient of each curve, means with SD, a rate against concentration graph and the R² of the fit.
- Criteria: Data analysis rewards showing how each rate was worked out. Evaluation can explain why the rate flattens at high concentration (limited enzyme).
2. Substrate type and yeast fermentation (C1.2, see cell respiration)
Research question: Which sugar (glucose, fructose, sucrose, maltose or lactose, each at 5 % w/v) gives the highest rate of carbon dioxide production by yeast at 35 °C, measured as gas volume in 10 minutes?
- Independent: type of sugar.
- Dependent: volume of carbon dioxide produced in 10 minutes (cm³).
- Controlled: sugar concentration, yeast mass, volume, temperature and apparatus.
- Method: Set up identical flasks in a water bath, each connected to a gas syringe or an inverted measuring cylinder. Include a no-sugar control, and use lactose as a sugar yeast should not be able to use.
- Statistics: Mean and SD for each sugar, error bars, and a t-test between the best two sugars.
- Criteria: Conclusion can compare your ranking with published information on which sugars yeast metabolise, with traceable citations.
3. Hydrogencarbonate and leaf-disc photosynthesis (C1.3, see photosynthesis)
Research question: What is the effect of sodium hydrogencarbonate concentration (0, 0.1, 0.2, 0.3 and 0.4 %) on the time taken for 5 of 10 spinach leaf discs to float under a lamp 20 cm away?
- Independent: hydrogencarbonate concentration (the carbon dioxide source).
- Dependent: time for half the discs to float (s), then 1/time as a rate.
- Controlled: light intensity and distance, temperature (use a heat shield), disc size, leaf age and leaf area.
- Method: Cut discs with a hole punch, remove the air with a syringe, then place 10 discs in each solution and record when each disc floats. Do three batches per concentration.
- Statistics: Mean time and SD, a graph of rate against concentration, and a comment on the limiting factor at high concentration.
- Criteria: Research design needs a justified range and an explained control for light and heat.
Cells: membranes and osmosis
4. Sucrose concentration and potato osmosis (D2.3, see water potential)
Research question: What is the effect of sucrose concentration (0, 0.2, 0.4, 0.6, 0.8 and 1.0 mol dm⁻³) on the percentage change in mass of potato cylinders after 60 minutes, and what concentration is isotonic to the potato tissue?
- Independent: sucrose concentration.
- Dependent: percentage change in mass of the cylinders.
- Controlled: potato variety and tuber, cylinder length and diameter, volume of solution, temperature and time.
- Method: Cut cylinders with a cork borer from one potato, blot and weigh, soak for a fixed time, then blot and reweigh. Use at least five cylinders per solution.
- Statistics: Mean percentage change with SD, a graph with error bars, and the x-intercept as the estimate of the isotonic concentration.
- Criteria: Conclusion: compare your intercept with published values for potato and discuss how its uncertainty affects the match.
5. Temperature and beetroot membrane permeability (B2.1, see membranes)
Research question: What is the effect of temperature (20, 30, 40, 50, 60 and 70 °C) on the permeability of beetroot cell membranes, measured as the absorbance of the pigment leaked from 1 cm³ cubes after 10 minutes?
- Independent: water temperature.
- Dependent: absorbance of the surrounding water, using a colorimeter with a blue-green filter.
- Controlled: cube size and surface area, washing time, volume of water, time in the bath and the same beetroot.
- Method: Rinse cubes until the water is clear, heat each in a water bath, cool the tubes, then zero the colorimeter on distilled water and read each tube.
- Statistics: Mean absorbance and SD, error bars and a curve, with a clear statement of the temperature range in which leakage rises.
- Criteria: Data analysis: record the colorimeter's uncertainty and carry it into the processed data.
Organisms: plants, genes and development
6. Wind and transpiration in a potometer (B3.1, see gas exchange)
Research question: What is the effect of fan distance from a leafy shoot (25, 50, 75 and 100 cm) on the rate of water uptake of a privet or coleus shoot, measured in a potometer as bubble movement over 5 minutes?
- Independent: fan distance, as a measure of air movement.
- Dependent: distance moved by the air bubble in 5 minutes (mm), converted to a volume using the capillary radius.
- Controlled: same shoot cut under water, leaf area, lamp position, humidity and temperature.
- Method: Assemble the potometer under water so there are no air leaks, let the shoot settle, then take three readings per distance, resetting the bubble each time.
- Statistics: Mean rate with SD, correlation between distance and rate, and uptake per cm² of leaf area (measured after the test).
- Criteria: Research design rewards a justified method for measuring leaf area.
7. Sun and shade leaves: stomatal density (B3.1, see gas exchange)
Research question: Is there a difference in mean stomatal density on the lower surface between leaves of the same plant species growing in full sun and in shade at one site, measured from nail-varnish impressions viewed at ×400?
- Independent: light environment (sun or shade).
- Dependent: stomata per mm².
- Controlled: species, leaf age, side of the leaf, position on the leaf and time of day.
- Method: Paint varnish on the lower surface, peel it with tape, and count stomata in at least five fields of view on each of 20 leaves per group. Calibrate the field area with a graticule.
- Statistics: Mean and SD per group, then a t-test, and say what the p-value means for your hypothesis.
- Criteria: Conclusion: compare with published stomatal density for the species and tie the result to adaptation.
8. Fast Plants F2 ratio and chi-squared (D3.2, see inheritance)
Research question: Do the numbers of purple-stemmed and non-purple Brassica rapa seedlings in an F2 generation fit the expected 3 : 1 ratio, tested with a chi-squared test at the 5 % significance level?
- Independent: genotype class, with a 3 : 1 expected ratio.
- Dependent: number of seedlings in each phenotype class.
- Controlled: seed batch, growing medium, light, temperature and watering.
- Method: Germinate F2 seeds on damp paper under a lamp and score each seedling for stem colour after about a week, ideally with a classmate checking your scoring. Check that a supplier will deliver to the UAE before you plan around it.
- Statistics: Chi-squared against 3 : 1 with 1 degree of freedom. Explain what the null hypothesis and critical value mean, and state the conclusion in words.
- Criteria: Evaluation: a small sample can give a poor fit, so say how big a sample is enough and why.
9. Salinity and seed germination (D3.1, see reproduction)
Research question: What is the effect of sodium chloride concentration (0, 50, 100, 150 and 200 mmol dm⁻³) on the percentage of radish seeds that germinate within 5 days at 25 °C?
- Independent: salt concentration.
- Dependent: percentage of seeds germinated, where germinated means a radicle at least 2 mm long.
- Controlled: seed batch, number per dish, volume of solution, temperature, light and dish type.
- Method: Place 20 seeds on filter paper in each of three dishes per concentration. Keep the paper moist with the same solution and count germinated seeds at the same time each day.
- Statistics: Percentage per dish, mean and SD, and a chi-squared test comparing the number germinating at 200 mmol dm⁻³ with the control.
- Criteria: Saline soils are a real issue in the UAE, which gives a specific context for the research question.
Ecosystems: fieldwork, data and modelling
10. Transect: distance from shore and plant diversity (C4.1, see populations and communities)
Research question: Is there a correlation between distance from the high-tide line (0 to 100 m along a transect) and the Simpson reciprocal index of plant species in 0.5 m² quadrats at one coastal site?
- Independent: distance along the transect.
- Dependent: Simpson reciprocal index for each quadrat, with soil conductivity as a second variable.
- Controlled: quadrat size, sampling time, the same transect and one observer.
- Method: Lay a tape, record plants in a quadrat every 10 m, and measure soil conductivity from a standard soil to water mix. Get permission, avoid protected species and do not uproot anything.
- Statistics: Simpson reciprocal index, a scatter graph, the correlation coefficient r and R², and a justified comment that correlation is not causation.
- Criteria: Evaluation: one transect is a limitation of the data, so explain how much that affects your conclusion.
11. Carbon dioxide and temperature from public data (D4.3, see climate change)
Research question: Is there a correlation between the annual mean atmospheric carbon dioxide concentration at Mauna Loa and the global mean surface temperature anomaly from 1960 to 2020?
- Independent: annual mean carbon dioxide concentration (ppm).
- Dependent: global temperature anomaly (°C).
- Controlled: data source, years used and baseline period.
- Method: Download the annual series from NOAA's Global Monitoring Laboratory and NASA's GISTEMP, record the exact versions, then clean and align the years in a spreadsheet.
- Statistics: Scatter graph, trend line, r and R². Both series rise with time, so discuss why that inflates r, and compare with published science. See also the Keeling curve note.
- Criteria: Database investigations focus the methodology on how you sampled and filtered the data.
12. Cytochrome c sequences and divergence time (A3.2 (HL) and A4.1, see molecular clocks)
Research question: Is there a linear relationship between estimated time since divergence from humans (million years) and the percentage difference in cytochrome c amino acid sequence for ten vertebrate species?
- Independent: divergence time, from the TimeTree database.
- Dependent: percentage difference in amino acid sequence, aligned using NCBI BLAST.
- Controlled: same protein, same reference species and same alignment settings.
- Method: Choose ten species across a range of divergence times, record accession numbers, align each with the human sequence, and keep a log of every step so another student could repeat it.
- Statistics: Scatter graph, line of best fit, r and R², then discuss whether a constant rate fits and where it does not.
- Criteria: Research design: explain why you chose these species and how reliable the database's divergence estimates are.
Ethics and safety: what to check before you start
Every idea above uses plants, yeast, food plant tissue, public data or a model, so none involves harming an animal. That is deliberate. The IB's animal experimentation policy applies to IB World Schools, including internal assessment, and is built on replacement, refinement and reduction. Experiments that harm or stress an animal are not acceptable, so do not choose one. Check the current IB guidelines with your teacher before you design anything involving animals or people.
- Do a written risk assessment and have your teacher approve it. Wear eye protection for peroxide, sodium hydroxide and acid.
- Fieldwork needs permission from the site, heat precautions and a plan not to damage plants or habitat.
- Human participants need informed consent and teacher approval, and body fluids should not be used, so none of these ideas needs either.
- Record every source as you go, because missing or improper referencing counts as academic malpractice.
Paper 1B asks data-based questions about experimental work, so a well-run IA is also practice for it: identifying variables, reading error bars and judging methods. Revise these skills on our IB Biology practicals page.
How to turn an idea into a research question
- Choose one syllabus topic you enjoy, then narrow it to a system you can name (one enzyme, one tissue, one plant, one data set).
- Name the independent and dependent variables, with units and the range, in the question itself.
- Add background theory that directly explains your prediction, not a general essay.
- Run a pilot to check the range, the measurement precision and whether a trend appears.
- List each control variable and how you will hold it constant, and decide your statistic before you collect data.
Pick the statistic from the question. Comparing two means suits a t-test, counts in categories suit chi-squared, and two changing variables suit a correlation coefficient with R². The skill is explaining what the test shows, which is covered in the data section of our IA guide. Use the glossary for command terms and the specification checklist to find topic codes.
If you want help sharpening a research question or planning your analysis, we offer 1-to-1 tutoring online and in Dubai. The report itself must be your own work.