Evaluation¶

Part of Module 1: Development of practical skills in biology.

Evaluation asks whether a result deserves confidence and, if not, why not. It identifies weaknesses in method and measurement, separates precision from accuracy, and proposes improvements that address the weakness actually found.

What You Need to Learn¶

Further detail: AS Biology A (H020) and A Level Biology A (H420).

How to judge a method and its results: spotting anomalies and sources of error, separating precision, accuracy, reliability and validity, calculating and discussing uncertainty, and suggesting specific, justified improvements.

Core Idea¶

A conclusion can be reasonable and still weak if the method did not control variables properly or the data were too uncertain.

An anomaly is a result that does not fit the pattern. It is not deleted. It is identified, and its possible cause is considered.

Precision is how close repeated measurements are to one another. Accuracy is how close a result is to the true value or to the intended measurement. A method can be precise and inaccurate, such as a balance that always reads 2 g too high.

Uncertainty comes from apparatus limits, human judgement and method design, and it is discussed explicitly.

Worked example: percentage uncertainty

A 25 cm³ measuring cylinder has an uncertainty of ±0.5 cm³.

Percentage uncertainty = (0.5 ÷ 10) × 100 = 5% when measuring 10 cm³

The same cylinder measuring 25 cm³ gives (0.5 ÷ 25) × 100 = 2%.

Measuring small volumes with a large instrument gives a large percentage uncertainty. Using a 10 cm³ pipette with an uncertainty of ±0.05 cm³ reduces the figure for 10 cm³ to 0.5%, which is a specific, justified improvement.

What Evaluation Should Cover¶

Whether the conclusion matches the evidence collected.
Whether the method really measured the biological variable of interest.
Whether uncontrolled variables could have altered the outcome.
Whether the apparatus was sensitive and precise enough.
Whether more repeats would improve reliability.

Useful Improvement Logic¶

Each improvement answers a particular weakness:

If timing is uncertain, use an automatic sensor or a clearer end point.
If colour judgement is subjective, use a colorimeter and not the eye.
If a biological sample varies naturally, increase the number of repeats or the sample size.
If conditions drift, control temperature, pH, light or concentration more carefully, for example with a thermostatically controlled water bath or a buffer.

Worked example: weakness, effect, improvement

Weakness	How it affected the result	Improvement
The end point of a starch test was judged by eye	The time recorded varied by several seconds between repeats, so the rate was imprecise	Use a colorimeter and take absorbance readings at fixed intervals
The tubes were not given time to reach the target temperature	The real temperature was lower than the recorded one, so the rate at each temperature was too low	Leave the tubes in the water bath for 5 minutes before mixing
Only one run at each temperature	One anomalous result could not be identified	Repeat three times and calculate a mean

Exam technique

Link every weakness to its effect on the result, and every improvement to its weakness. "Be more careful" and "use more accurate equipment" score nothing. "Use a colorimeter, because judging the colour by eye is subjective and reduces precision" does.

Common Pitfalls¶

Vague improvements such as "be more careful".
Naming a weakness without its effect on the result.
Confusing accuracy with precision.
Treating every anomaly as a mistake, when it can be genuine biological or method variation.

Applied Contexts¶

Enzyme practicals raise temperature control, subjective colour end points and mixing delays.
Potometer work raises leaks, air bubbles and the fact that uptake is an estimate of transpiration and not a direct measurement.
Field sampling raises sample size, bias and whether the sample represented the habitat fairly.

PAG-Linked Evaluation Patterns¶

Microscopy can fail through poor calibration, unclear staining or crushed specimens, so measurement error and image quality are judged together.
Colorimetry is usually stronger than judging colour by eye, but only if the blank, filter and cuvette handling are themselves consistent.
Chromatography can separate poorly if the starting spot is too large or if similar substances have similar Rf values, so identification may remain uncertain.
Microbial work should always be questioned for contamination, unequal inoculation and inconsistent incubation conditions.
Response investigations often show genuine variation between organisms, so repeats, sample size and control comparisons matter more than one dramatic result.

Common Confusions¶

Precise and accurate: precise results agree with each other, and accurate results agree with the true value. Both are needed for good data.
Reliable and valid: reliability is about consistency on repeating. Validity is about measuring the right thing.
Error and uncertainty: an error is a mistake or a deviation. Uncertainty is the range within which the true value is expected to lie.

Check Yourself¶

Distinguish between accuracy and precision.
A 50 cm³ measuring cylinder (uncertainty ±0.5 cm³) is used to measure 20 cm³ of liquid. Calculate the percentage uncertainty.
A student's repeats are 12.1, 12.3, 12.2 and 15.8 s. Identify the anomaly and state what should be done about it.
Suggest one improvement for judging the end point of a colour change, and justify it.
A potometer is used to compare transpiration in two conditions. Explain one limitation of the data.
Explain why a reliable set of results does not guarantee a valid conclusion.

Answers

Accuracy is closeness to the true value. Precision is closeness of repeated measurements to one another.
(0.5 ÷ 20) × 100 = 2.5%.
15.8 s is the anomaly, because it does not fit the other three. It should be identified and checked for a cause, and a mean calculated with and without it. It should not be deleted without comment, and a further repeat could be done.
Use a colorimeter and measure absorbance, because it gives objective numerical values and removes the subjectivity of judging the colour by eye.
It measures water uptake, not transpiration directly, because some of the water taken up is used in the plant. It is only an estimate of water loss.
Reliable results are consistent, but if the method did not measure the variable the question asks about, or left an important variable uncontrolled, the results can be consistently wrong, so the conclusion would not be valid.

Key Terms¶

Accuracy: closeness of a result to the true value or to what was intended to be measured.
Precision: closeness of repeated measurements to one another.
Uncertainty: the doubt attached to a measurement because of apparatus limits or method design.
Reliability: the extent to which repeated measurements give consistent results.
Validity: the extent to which the investigation measured the biological factor it was supposed to measure.
Improvement: a specific change to method or apparatus that directly addresses an identified weakness.