6.3.2Inquiry Process
Evaluating in Biology — IB Biology HL Revision Notes & Topical Questions
5 min read 6 parts Lesson 160 of 160
Part 1 of 6Evaluating the Hypothesis
The evaluation follows on from the conclusion and asks how far the evidence can be trusted.
Evaluating the Hypothesis
Strength of support
- Even if the data support the hypothesis, comment on how firmly, taking the uncertainties and errors you found into account.
Spread of data
- A large standard deviation at one condition, such as 50 °C in an enzyme study, weakens claims made about that exact point.
Effect on result
- Say how an error could change the key outcome, for example by shifting an optimum value or hiding a difference between means.
Part 2 of 6Types of Error
Distinguishing the two types of error is the most important part of an evaluation.
Systematic error
- A fault in method or apparatus that pushes every result the same way, such as a pH meter that always reads 0.5 units too high.
Random error
- Unpredictable variation between measurements, often caused by natural differences between biological samples such as potatoes.
Systematic example
- Water evaporating from potato cylinders before final weighing makes every final mass too low and exaggerates water loss.
Random example
- Judging a colour change endpoint by eye gives results that vary with reaction time and perception.
Reducing them
- Repeats and averaging reduce random error, while calibration or a changed procedure is needed to remove systematic error.
| Property | Systematic error | Random error |
|---|---|---|
| Direction | Always the same way | Varies either way |
| Affects | Accuracy | Precision |
| Removed by | Better method or calibration | More repeats and averaging |
Part 3 of 6Weaknesses, Limitations and Assumptions
Weakness
- A feature of the method causing significant error, such as measuring a non-circular inhibition zone with a ruler where parallax is large.
Limitation
- A factor restricting how far the conclusion applies, such as testing only one potato species so results cannot be generalised to others.
Assumption
- A simplification made in the design, such as assuming mass changed only because of water movement when ions may also have leaked out.
Control gaps
- Variables that were not fully controlled, like temperature drifting in a water bath, reduce the validity of the comparison.
Part 4 of 6Proposing Improvements
1
Take each significant weakness or error identified and pair it with one specific improvement that targets that exact problem.
2
Make sure the change is realistic for a school laboratory, for example using a colorimeter or digital image analysis instead of judging by eye.
3
Explain how the improvement would reduce the error, such as removing human judgement of an endpoint so timings become consistent.
4
Where useful, suggest extending the work with more species or a narrower range of the independent variable to widen the conclusion.
Part 5 of 6Worked Example
Question
- A 10.0 cm³ volume is measured with a measuring cylinder of uncertainty ±0.5 cm³.
- Calculate the percentage uncertainty and compare it with a burette of uncertainty ±0.05 cm³.
- Percentage uncertainty (cylinder) = (0.5 ÷ 10.0) × 100 = 5%.
- Percentage uncertainty (burette) = (0.05 ÷ 10.0) × 100 = 0.5%.
- Ratio = 5 ÷ 0.5 = 10, so the burette is ten times less uncertain for this volume.
Answer The cylinder gives 5% uncertainty and the burette 0.5%, so switching to a burette is a relevant and realistic improvement.
Part 6 of 6Exam Checklist & Tips
6.3.2 – Key Points for the Exam
- Systematic error An error that shifts all measurements in the same direction and reduces accuracy.
- Random error Unpredictable variation in measurements that reduces precision.
- Limitation A factor that restricts the scope over which a conclusion can be applied.
- Assumption A simplification accepted in the design of an experiment without being tested.
- Percentage uncertainty The absolute uncertainty divided by the measured value, times 100.
Common Mistakes
- Listing vague errors such as human error instead of naming a specific source and how it affected the data.
- Suggesting improvements that are unrealistic in a school laboratory or that do not address the stated weakness.
- Mixing up limitations, which restrict the conclusion, with weaknesses, which cause error in the data.
- Saying more repeats will fix a systematic error, when repeats only reduce random error.
Last updated:
\n