6 Experimental skills
- Syllabus
- 2024
- Section
- 6
- Level
- —
(a) Problem-solving and application
- Syllabus
- 2024
- Topic
- —
- Level
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Turn a practical context into a solvable problem
Solving a practical problem means extracting the relevant variables and evidence, choosing a valid comparison or relationship, and producing an answer with units and a context check.
| Move | What to do |
|---|---|
| 1 state the target | identify exactly what must be found: difference, rate, percentage, optimum or predicted outcome |
| 2 map the variables | identify the changed condition and the measured response; ignore details that do not affect the target |
| 3 align the evidence | compare like with like—same time, unit, sample basis or body-mass basis |
| 4 choose an operation | difference = final − initial; rate = change ÷ time; percentage change = change ÷ original × 100 |
| 5 calculate transparently | show selected values, substitution, arithmetic and units |
| 6 check the context | confirm sign, size and unit are biologically plausible and answer the stated target |
In a seedling table at day 20, dry mass is 8.5 g with fertiliser and 6.8 g without it. The matched difference is 1.7 g; relative to the no-fertiliser value, the increase is 1.7 ÷ 6.8 × 100 = 25%.
Do not compare unmatched days or switch denominators silently. A numerical pattern is an answer to the calculation; explaining why it occurs requires relevant biological knowledge and belongs to the next reasoning step.
Apply biological knowledge to a practical observation
Applying knowledge in a practical context means selecting the biological principle that controls the measured outcome and building an explicit causal link from condition to observation.
| Reasoning move | Example of a complete link |
|---|---|
| identify the observation | fertilised seedlings have greater dry mass at the same time |
| select relevant biology | nitrate supports amino-acid and protein synthesis; magnesium supports chlorophyll |
| connect mechanism to measurement | mineral supply can increase growth and photosynthesis, producing more biomass and therefore greater dry mass |
| respect measurement choice | dry mass excludes variable water content, so it better represents accumulated biological material |
| transfer to another context | a mouse has a larger surface-area-to-volume ratio than a human, loses heat faster per gram and needs a higher respiration rate per gram to maintain temperature |
A strong explanation has the form condition → biological process → effect on the measured variable. Use the context's scale and units: total oxygen use and oxygen use per gram answer different questions.
Naming a topic is not application. 'Fertiliser helps growth' or 'surface area matters' is incomplete until the mechanism is connected to the actual result, measurement basis and direction of change.
(b) Investigation planning and methods
- Syllabus
- 2024
- Topic
- —
- Level
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Build an investigation that answers the question
A valid investigation plan is a connected chain: change one relevant condition, measure a response that answers the question, keep other influential conditions consistent, repeat the comparison, and state exactly how the evidence will be collected.
| Planning move | Decision to record |
|---|---|
| 1 define the outcome | translate the question into a measurable response, such as gas volume per minute, mass lost in a fixed time, or number of visits in ten minutes |
| 2 choose the change | use a sensible range or clear comparison for the condition being investigated; include a reference condition when it makes the effect interpretable |
| 3 select the technique | choose apparatus and a measurement that genuinely indicate the biological process, with units and a stated time interval |
| 4 control other influences | keep relevant biological material and environmental conditions the same so they cannot provide a rival explanation |
| 5 make evidence dependable | repeat each condition using independent samples and calculate a representative result such as a mean |
| 6 specify the comparison | state which results will be compared to answer the original question |
For decomposition at different temperatures, use equal masses and the same type of plant material and decomposer. Keep moisture, pH and oxygen availability consistent. Measure carbon dioxide volume produced in the same time at each stated temperature, repeat each temperature, then compare mean volumes per unit time.
A list of apparatus is not a plan. Each item must have a purpose, and the measured response must connect to the biological process. Changing several conditions together prevents the result from showing which condition caused the difference.
Choose an appropriate and safe practical method
An experimental method is appropriate when its apparatus, sequence and measurement produce evidence that answers the aim. It is safe when each real hazard is identified and controlled without preventing the method from working.
| Experimental need | Appropriate technique and reason |
|---|---|
| measure gas production | collect gas in a gas syringe or inverted measuring cylinder; this gives a volume rather than an uncertain bubble count |
| measure a rate | record a defined change and divide by a stated time, or measure the time taken for a defined change |
| maintain temperature | use a thermostatically controlled water bath, check with a thermometer and allow samples to reach the set temperature before timing |
| compare biological samples | use the same species and matched starting size, mass, age or condition where these could affect the response |
| handle measured quantities skilfully | zero the instrument, read scales at eye level where relevant, use suitable units and apply the same timing and endpoint to every condition |
Plan safety as hazard → possible harm → specific control. For hot apparatus, use a heatproof mat and suitable handling tools and allow equipment to cool; for irritant solutions, wear eye protection and avoid skin contact; for microorganisms, use clean technique, keep cultures contained and wash hands. Use only controls relevant to the stated method.
Writing 'be careful' or listing gloves and goggles for every investigation is not a safety method. A precaution is useful only when it reduces an identified risk. Likewise, precise apparatus cannot rescue a method that measures the wrong outcome or changes its procedure between conditions.
(c) Observations, measurements and variables
- Syllabus
- 2024
- Topic
- —
- Level
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Measure, record and present observations precisely
A useful observation records what is detected; a measurement adds a numerical value and unit. Appropriate precision means using an instrument whose scale can distinguish the changes that matter and recording only the detail that instrument supports.
| Evidence move | Methodical decision |
|---|---|
| observe | describe visible changes with agreed categories, or select a measurable quantity instead of relying on vague words such as 'more' |
| measure | choose a suitable instrument, zero it where required, read its scale consistently and include the correct unit |
| record | enter each result immediately; put the changed condition in the first column and repeat readings in separate columns |
| process | keep raw readings, calculate a mean when repeats are available and mark rather than silently delete an anomalous value |
| present | use a table for exact values, a line graph for a continuous relationship, or a bar chart for separate categories |
If leaf-rise time is measured with a stopwatch reading to 0.1 s, record results such as 12.4 s rather than 12 s or 12.437 s. A results table can show light distance, three rise times and a mean; a line graph is appropriate because distance varies continuously.
Extra decimal places do not create extra precision. They are justified only by the measuring instrument and method. Also keep units in table headings or axis labels rather than mixing them unpredictably into individual data cells.
Identify variables by their role in the investigation
A variable is identified by the role it plays in a particular investigation: the independent variable is deliberately changed, the dependent variable is measured as the response, and control variables are kept constant so they do not alter that response.
| Role | Question to ask | Photosynthesis-rate example |
|---|---|---|
| independent variable | what is deliberately changed between conditions? | distance of a lamp from pondweed |
| dependent variable | what response is measured, with a method and unit? | oxygen volume produced per minute |
| control variables | what other factors could change the response and therefore must remain constant? | pondweed species and length, light colour, temperature, carbon dioxide availability and measurement time |
Read an aim in the form 'the effect of X on Y': X is usually the independent variable and Y the dependent variable. Then use biological knowledge to identify plausible alternative causes of Y and control the ones the method can reasonably hold constant. A variable may be biotic, such as species or starting mass, or abiotic, such as temperature, pH or light intensity.
A control variable is not the same as a control group. A control variable is a condition kept constant across all groups; a control group or reference condition is one comparison group. Time is the dependent variable only when time itself is measured as the response—otherwise it may be controlled.
(d) Analysis, communication and evaluation
- Syllabus
- 2024
- Topic
- —
- Level
- —
Turn experimental data into a bounded conclusion
Analysing experimental data means identifying the supported pattern, quantifying it where possible, using relevant biological knowledge to interpret it, and limiting the conclusion to what the evidence actually shows.
| Reasoning move | What a defensible statement does |
|---|---|
| read the evidence | compare matched groups, axes, units, time points and sample sizes before selecting a pattern |
| describe the pattern | state direction, range, optimum, plateau or difference and support it with representative values |
| check variation | identify overlap, spread, anomalies or exceptions rather than hiding them |
| interpret biologically | connect the pattern to a relevant mechanism, keeping the causal direction explicit |
| conclude within limits | answer the aim, distinguish association from causation and avoid extending beyond the organisms, conditions or range tested |
If sperm motility falls from about 70% at zero drug to about 5% at 0.4 arbitrary units and remains similar at 0.8, the supported pattern is a steep decrease followed by a plateau. Concentration changes little across the same range, so the drug affects movement much more clearly than sperm number in these mice.
A conclusion is not a restatement of every number, and a biological explanation cannot replace description of the data. 'The treatment works' is too broad unless the measured outcome, comparison, variation and study boundary support that claim.
Communicate experimental findings so they can be checked
Experimental findings are communicated clearly when the calculation, table, graph and written conclusion all describe the same variables, units and comparison using precise biological language.
| Information | Appropriate communication |
|---|---|
| exact readings and repeats | a ruled table with informative headings, units in headings and consistent precision |
| continuous independent variable | a line or scatter graph with a sensible scale and points plotted accurately; add a best-fit line or curve only when justified |
| separate categories | a bar chart with separated bars and clearly named categories |
| calculated change | show the operation, values and unit or percentage so the result can be checked |
| conclusion | name the variables, direction or difference, relevant values and any important qualification |
percentagechange=((finalvalue−initialvalue)/initialvalue)×100
A graph title alone does not define the evidence: both axes need variables and units. Avoid vague phrases such as 'it went up a lot'; state what changed, by how much or across which range, and whether the pattern has an exception.
Judge whether experimental results are reliable
Reliability is the extent to which repeated measurements or independent repeats give consistent results. It is assessed from repetition, sample size and the spread of results—not from whether the result matches an expectation.
| Evidence about reliability | Interpretation |
|---|---|
| repeated readings are close together | low random variation; the result is more repeatable |
| repeated readings are widely spread | high random variation; a mean alone may hide instability |
| only one reading or a very small sample | consistency cannot be judged securely |
| similar result from more samples, people, times or investigators | stronger reproducibility and less dependence on one case |
| an anomalous value | investigate and repeat; exclude it only with a stated evidence-based reason |
Improve reliability by taking independent repeats at every condition, increasing a relevant sample size, using the same defined method and reporting the spread as well as a mean. A longer study can reveal whether an apparent effect persists.
Repeated measurements can agree closely and still all be wrong because of the same calibration error. That result is reliable but not accurate. Repeating a biased or invalid method does not remove the bias or make it test the intended question.
Evaluate accuracy, validity and the conclusion
Evaluation identifies the specific weakness, explains how it could affect the result or conclusion, and proposes a change that addresses that cause. Accuracy concerns closeness to the accepted or true value; validity concerns whether the method and evidence test the intended question.
| Evaluation target | What to inspect | Targeted improvement |
|---|---|---|
| measurement accuracy | calibration, zero error, instrument resolution, parallax and subjective endpoints | calibrate or zero apparatus, use a finer suitable instrument, read correctly, or standardise the endpoint with a colour reference or sensor |
| method validity | control of alternative causes, a fair comparison and whether the dependent measure represents the intended process | control the influential variable, add an appropriate reference group, or measure the process more directly |
| biological validity | sample size and diversity, model organism, laboratory versus natural conditions, and duration | use a larger representative sample, relevant organisms or conditions, and a sufficient observation period |
| conclusion validity | whether the claim exceeds the data range, measured outcome or tested population | narrow the claim and state the evidence boundary |
For every limitation, write a causal link: weakness → effect on measurement or comparison → consequence for the conclusion. For example, judging browning by eye gives a subjective endpoint, so recorded times may differ between observers; a standard colour chart or colorimeter makes the endpoint consistent and more accurate.
Generic statements such as 'use better equipment', 'repeat it' or 'human error' are not evaluations. Name the source of error and show why the proposed change reduces it. Repeats mainly address random variation; they do not automatically correct a systematic error or an invalid comparison.