Evaluating in Biology (DP IB Biology): Revision Note
Evaluating in Biology
The evaluation is a critical reflection on your investigation's methodology
This is where you demonstrate your understanding of the scientific process by identifying the weaknesses and limitations of your own work
The goal is to assess the quality of your data and its impact on your conclusion, and to suggest meaningful, realistic improvements
Principles of evaluation
Evaluate your hypothesis
This is the final comment on your hypothesis, which should follow on from your conclusion
Even if your data supported your hypothesis, you should evaluate the strength of this support in light of the uncertainties and errors you have identified
For example:
While the data supported the hypothesis, the large standard deviation observed at 50°C suggests that the enzyme's activity was becoming unpredictable
This indicates that the point at which denaturation begins may be less precise than the data suggests, weakening the conclusion about the exact upper temperature limit
Identify and discuss sources of error
This is the most important part of your evaluation
You must identify and discuss specific sources of error in your procedure, distinguishing between the two main types
Systematic errors:
These are flaws in the experimental method or apparatus that cause the result to be consistently wrong in the same direction (e.g., always too high or always too low)
Example 1:
An incorrectly calibrated pH meter that always reads 0.5 pH units too high
This would shift the entire graph for an enzyme pH experiment, leading to an inaccurate value for the optimal pH
Example 2:
Evaporation of water from the surface of potato cylinders before the final mass is measured
This would cause the final mass to be consistently lower, making the calculated water loss appear greater than it actually was
Random errors:
These are unpredictable variations in measurements that occur by chance
In biology, these are often caused by the natural variability within biological samples
Example 1:
Natural variation in the water potential between different potatoes, or even within different parts of the same potato
Example 2:
Subjectivity and variations in reaction time when judging the endpoint of a colour change in an enzyme assay by eye
Random errors can be minimised by taking multiple repeat trials and calculating an average
Evaluate methodological weaknesses, limitations and assumptions
Beyond errors, you should also discuss other aspects of your method that affect the quality of your conclusion
Weaknesses:
These are the aspects of your method that lead to significant systematic or random errors
For example:
Using a ruler to measure the change in diameter of the zone of inhibition in an antibiotic test is a key weakness, as the zones are often not perfectly circular and parallax error is significant
Measuring the area using digital image analysis would be more accurate
Limitations:
These are factors that limit the scope of your conclusion
For example:
This investigation was limited to using one species of potato, Solanum tuberosum
Therefore, the conclusion that the isotonic point is 0.35 M cannot be generalised to other plant species, which may be adapted to different osmotic environments
Assumptions:
These are simplifications made during your experiment
For example:
It was assumed that the potato cylinders were only gaining or losing mass due to the movement of water
In reality, a small amount of sucrose may have diffused into the cells, or ions may have leached out of the cells, introducing a minor error
Explain realistic and relevant improvements
For every significant weakness or source of error you identify, you must suggest a specific, realistic improvement
The improvement must be relevant
It should directly address the weakness you identified
The improvement must be realistic
You should be able to carry it out in a typical school laboratory
Worked Example
Research question:
"What is the effect of pH (from pH 4 to pH 10) on the rate of activity of the enzyme trypsin in breaking down casein protein?"
Weakness 1 (systematic error)
The reaction endpoint is judged by eye (when the mixture “looks clear”)
Impact:
Some runs will be stopped earlier/later than others
This consistently biases times (and therefore rates), especially at intermediate pH where clearing is gradual
It reduces accuracy and can flatten the true pH–activity curve
Realistic improvement:
Use a colorimeter with fixed wavelength (e.g., ~400–440 nm) and set an objective absorbance threshold (e.g., stop timing when A ≤ 0.05 above a water blank)
Use matched cuvettes, a casein blank for zeroing, and record absorbance every 2–3 s or use a data logger to detect the threshold automatically
Weakness 2 (random error)
Variability in mixing the enzyme and substrate or pH drift (e.g. CO₂ absorption, buffer not fully equilibrated to 37 °C)
Impact:
This can cause trial-to-trial scatter in times
This increases standard deviation, making differences between adjacent pH values less clear
Realistic improvement:
Pre-equilibrate separate tubes of buffer, casein, and trypsin at 37 °C for ≥10 min; cap tubes to limit gas exchange
Mix reactions in a consistent way (e.g., invert exactly 3× or use a small sterile stir bar at fixed speed) and start the timer at the same moment (enzyme addition)
Measure and confirm pH at 37 °C with a calibrated pH meter (pH can shift with temperature)
Increase replicates from 3→5 at each pH and report mean ± SD
Limitation
Trypsin activity can decline over the course of the practical (room-temperature handling, repeated warming/cooling, autolysis)
Impact:
Trypsin activity can decline over the course of the practical (room-temperature handling, repeated warming/cooling, autolysis)
If pH conditions are always run in the same order (e.g., 4→10), later conditions may appear slower for reasons unrelated to pH, confounding the conclusion
Realistic improvement:
Prepare single-use aliquots of trypsin; keep on ice until needed; discard leftovers
Randomise or counter-balance the order of pH runs (e.g., 7, 4, 9, 6, 10, 5, 8) so any activity drift is spread evenly
Limit total session time; if long, split into blocks with fresh enzyme each block
Worked Example
Research question:
"What is the isotonic point of potato tuber tissue in a sucrose solution?"
Weakness 1 (systematic error):
The potato cylinders were blotted dry with paper towels before the final weighing
Impact:
This method is not consistent.
Some cylinders may have been blotted more thoroughly than others, and some surface water may have remained
This would lead to a final mass that is inaccurately high, especially for cylinders that were in hypotonic solutions
This reduces the accuracy of the determined isotonic point
Realistic Improvement:
A more consistent method, such as a "salad spinner" protocol, could be used
All cylinders could be placed in a spinner for a set time (e.g., 10 seconds) to remove excess surface water in a highly repeatable way
Weakness 2 (random error):
Natural biological variability between the potato cylinders
Impact:
Despite efforts to control their size, the cylinders will have had slight differences in their cellular structure and initial water potential
This inherent variability is the most likely cause of the scatter of data points around the line of best fit, as shown by the standard deviation error bars
Realistic Improvement:
The impact of this random error could be further minimised by increasing the number of replicates from three to five for each sucrose concentration
This would make the calculated mean percentage change in mass a more reliable estimate
Limitation:
The experiment was conducted at a single room temperature
Impact:
The conclusion is only valid for the temperature at which the experiment was conducted (e.g. 21°C)
Temperature affects the kinetic energy of water molecules and can alter the permeability of cell membranes, so the isotonic point may be different at other temperatures
Realistic Improvement:
The investigation could be extended by repeating the entire experiment in temperature-controlled water baths set at different temperatures (e.g. 10°C, 20°C, 30°C) to determine how temperature affects the isotonic point
Examiner Tips and Tricks
Be specific
Never blame "human error"
Instead of saying "My measurements were wrong," identify a specific source of error, like "The parallax error when reading the volume in the measuring cylinder could have led to inconsistent volumes of solution being used."
Prioritise your evaluation
Focus on the one or two most significant sources of error that had the biggest impact on your final result
Discussing the biological variability of your samples is often a key point in a biology evaluation
Close the loop: Weakness → Impact → Improvement
For every weakness you identify, you must explain its impact on your final result and then suggest a specific improvement to fix it
Evaluate your own data.
Do not write a generic evaluation that could apply to any experiment
Refer back to your own results, graphs, and observations
For example, "The large standard deviation at 40°C, as shown by the error bars, suggests that the reaction became unpredictable at this temperature, reducing the reliability of the calculated mean."
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