Teaching uncertainty in A Level Biology: linking theory to practical work
16 March 2026
Charlotte Rhodes, Head of Biology at Bradfield College
Uncertainty is a fundamental part of scientific practice, yet for many A Level Biology students it can feel just a formula to memorise and apply. Too often, calculations of uncertainty sit apart from practical work, disconnected from decisions about experimental design, apparatus choice and the strength of conclusions.
In this blog, Charlotte Rhodes, Head of Biology at Bradfield College, shares how she helps students move uncertainty beyond a mathematical exercise and into the heart of investigative thinking in Cambridge OCR A Level Biology A and A Level Biology B.
Why uncertainty matters and why students struggle
Students often complete a school-based practical and confidently announce in their conclusion that they have “proved” a theory. As science teachers, we know our role is to help them recognise the limitations of the work they carry out and the conclusions they draw. One area that students find particularly challenging is understanding that conclusions are only as strong as the measurements behind them – and that all measurements have limits. This is why uncertainty matters.
In A Level Biology, uncertainty can sometimes be approached as a purely mathematical exercise: learn the formula, apply it correctly, move on. While calculating uncertainty is a required mathematical skill in Cambridge OCR AS and A Level Biology (see the maths skills handbook, Section M1.11), students often struggle to connect the calculation to the bigger picture. They may be able to determine a percentage uncertainty, but find it harder to link this to the practical endorsement when planning an investigation, selecting appropriate apparatus or evaluating the quality of their data.
For this reason, I have found it important to move uncertainty beyond the formula. Without explicit and meaningful links to practical activities, it risks becoming a procedural task rather than a conceptual tool. My aim is to help students see uncertainty as part of scientific judgement: something that shapes how we design experiments and how confidently we interpret our results.
Using apparatus choice to build conceptual understanding
How many times have you seen an apparatus list that states ‘measuring cylinder’ and a method saying ‘measure out 20cm3 using the measuring cylinder’? There are many different sizes of measuring cylinder, so which once should you use?
Within the practical skills assessment, skill 1.2.1(a), Apply investigative approaches and methods to practical work (see the practical skills handbook), includes the selection of appropriate apparatus. If students understand the role uncertainties play in these scenarios, they will be able to fully justify their choices.
Enzymes investigations and uncertainties
I start introducing this planning approach when studying 2.1.4, Enzymes. PAG 4 activities (rates of enzyme-controlled reactions) provide an excellent opportunity to get students to start considering the different variables and how to select appropriate equipment. This helps them to build in their investigative skill (1.2.1(a)). The portable and flexible PAG tracker allows you to amend the skills assessed in each practical.
Before completing the practical work on PAG4.2, The effect of enzyme concentration on the rate of a reaction, we spend some time considering the selection of appropriate equipment.
Having previously taught the students the mathematical skill of uncertainty, they each pick three different pieces of standard laboratory equipment for measuring volumes. They record the absolute uncertainty for each piece of equipment on a mini whiteboard. To select the most appropriate equipment, students needed to know the total volume required. The next step was to provide them with an edited version of the PAG method, which included just the step-by-step instructions, with all the equipment references removed. This meant they had to think for themselves about what they’d need and why, rather than simply following what was written.
They then highlight the different volumes that needed to be measured (ignoring the serial dilutions at this point) and calculated the percentage uncertainty for that specific volume, for each of the pieces of equipment they had previously chosen.
Challenging misconceptions
This activity uncovered a few misconceptions:
- Students often assume that a syringe would be the best piece of equipment, because it has a higher resolution.
- Students were surprised to find that different apparatus with the same overall size and graduations resulted in the same percentage uncertainty. But we also had a lightbulb moment when they could use the maths to explain this.
- Students assumed the smallest equipment with the smallest increments would be the best to use, which wasn’t always the case when multiplying up the absolute uncertainty.
We completed the task as a class, collating all the measurements on the board and selecting the apparatus with the smallest percentage uncertainty. By removing the named apparatus from the method, the students were forced to justify their choice, using mathematical rules to support it. They were able to appreciate how significant the difference in uncertainty between different pieces of equipment was. This challenged their ideas of ‘it’s just 2cm3’ and changed their approach to later practical work.
Developing understanding through practical comparisons
Later on in the course uncertainties become more powerful when students compare the impact of the same equipment used across different contexts. The practicals related to 2.1.6, Plasma membranes, particularly PAG 5.1 (the effect of temperature on membrane permeability) and PAG 8.1 (an investigation into the water potential of potato), work well here. The preparation of plant tissues in these practicals lends itself perfectly to comparing uncertainties and evaluating the impact on results.
In PAG 5.1, students cut beetroot cylinders to a length of 5mm. In PAG 8.1, they cut cylinders of potato to 40mm. If they do this using the same ruler for both activities, which therefore has the same absolute uncertainty, they are able to calculate the overall percentage uncertainty to show a substantial difference.
Again, many students assume because they used the same ruler, the uncertainty is the same. If they were to guess, they would say the beetroot was ‘better’, as being the smallest, it must have a lower uncertainty. It does have the largest surface area to volume ratio, but the percentage uncertainty is much greater. If students then apply this level of uncertainty to both sets of results, they can see the difference in the potential error that can exist.
When they then come to evaluate their practical work, they now understand how the resolution of the equipment used directly links to the percentage uncertainty of the results and the impact this has on their conclusion. This also helps them better understand the exam-based questions that ask them to improve a method. By considering if they are using the correct equipment, with the most appropriate resolution, they can get a better handle on this.
Adding a focus to these PAG activities on uncertainty shows students that it isn’t just about choosing equipment – it’s about interpreting the quality of their data and judging the strength of their conclusions.
Conclusion
Uncertainty is more than a calculation at the end of a practical. When it is embedded into planning and evaluation, students begin to see how it influences the quality of their data and the strength of their conclusions. Small adjustments to how we frame practical work – such as removing prescribed equipment or explicitly comparing percentage uncertainties – can shift uncertainty from a mathematical exercise to a tool for scientific judgement.
Over time, this helps students think more critically about the measurements they take and the claims they make, strengthening both their practical skills and their confidence in evaluation.
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About the author
Charlotte Rhodes is Head of Biology at Bradfield College. She teaches OCR A Level Biology and has a particular interest in developing students’ investigative skills and confidence in practical work. Charlotte is passionate about helping students move beyond procedural understanding to think more critically about data, measurement and evaluation. She enjoys sharing practical strategies that support both conceptual understanding and success in assessment.
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