Evaluating in Biology (DP IB Biology): Revision Note

Ruth Brindle

Last updated

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

    1. 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

    2. 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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Ruth Brindle

Author: Ruth Brindle

Expertise: Biology Content Creator

Ruth graduated from Sheffield University with a degree in Biology and went on to teach Science in London whilst also completing an MA in innovation in Education. With 10 years of teaching experience across the 3 key science disciplines, Ruth decided to set up a tutoring business to support students in her local area. Ruth has worked with several exam boards and loves to use her experience to produce educational materials which make the mark schemes accessible to all students.