What you'll learn

• How to turn a biological question into a testable practical investigation.
• How to identify independent, dependent and control variables.
• How to choose suitable apparatus, equipment and techniques.
• How to judge whether a method is appropriate for the expected outcome.

Why planning matters

In OCR A-Level Biology A, planning is not just “writing a method”. It is the skill of designing an investigation that can actually answer the biological question being asked.

A good plan shows that you can:

• apply biological knowledge to a practical context
• choose sensible equipment and techniques
• control variables fairly
• collect data that can be analysed
• evaluate whether the method is valid and reliable

This is part of Module 1, so it can appear in written papers in almost any practical-flavoured question.

The overall planning sequence

A practical plan usually follows a logical order: question → prediction → variables → method → data → evaluation.

Starting with a research question

A research question is the specific question your investigation aims to answer. It should name the factor being changed and the outcome being measured.

Weak question:
“How does temperature affect enzymes?”

Better question:
“How does temperature affect the rate of amylase-catalysed starch breakdown?”

The better version is clearer because it names:

• the independent variable: temperature
• the dependent variable: rate of starch breakdown
• the biological system: amylase acting on starch

Prediction and hypothesis

A prediction is what you expect to happen. A hypothesis is a scientific explanation for that prediction.

For example:

• Prediction: As temperature increases from 10 °C to 40 °C, the rate of amylase activity increases.
• Hypothesis: Higher temperature increases kinetic energy, causing more frequent enzyme-substrate collisions, until the enzyme begins to denature at higher temperatures.

Identifying variables

Variables are the factors in an investigation that can change.

Independent variable

The independent variable is the factor you deliberately change.

Examples:

• temperature in °C
• pH
• light intensity in lux
• substrate concentration in mol dm⁻³
• time in s

Dependent variable

The dependent variable is the factor you measure as the outcome.

Examples:

• rate of reaction in cm³ s⁻¹
• change in mass in g
• length of root growth in mm
• number of bubbles per minute
• absorbance using a colorimeter

Control variables

A control variable is a factor that must be kept constant so that it does not affect the dependent variable.

For example, if you investigate the effect of temperature on amylase activity, you should control:

• pH, using a buffer solution
• enzyme concentration
• substrate concentration
• volume of enzyme and substrate
• time allowed for the reaction, if measuring endpoint colour change

Confounding variables

A confounding variable is an uncontrolled factor that affects the dependent variable, making it harder to tell whether the independent variable caused the result.

For example, if you compare plant growth in different light intensities but also give the plants different volumes of water, water availability becomes a confounding variable.

Choosing apparatus, equipment and techniques

When selecting equipment, ask: “Is this apparatus suitable for the measurement I need?”

Important features include:

• range: the values the apparatus can measure
• resolution: the smallest change the apparatus can detect
• precision: how close repeated measurements are to each other
• accuracy: how close a measurement is to the true value

Examples of suitable choices

For volume:

• measuring cylinder: suitable for approximate volumes
• pipette or burette: better for accurate volumes

For mass:

• two-decimal-place balance: suitable for changes in potato mass
• top-pan balance with low resolution may be unsuitable for very small samples

For colour change:

• visual judgement may be subjective
• colorimeter gives quantitative absorbance data

For temperature:

• water bath is usually better than leaving tubes on a bench
• thermometer or temperature probe checks actual temperature

Designing a method that answers the question

A practical method should be detailed enough that another student could repeat it and collect comparable data.

You should include:

• the range of independent variable values
• the intervals between values
• the number of repeats
• how control variables are maintained
• how the dependent variable is measured
• any safety or ethical considerations
• the planned results table or graph

Range and intervals

The range is the spread between the lowest and highest value of the independent variable.

The interval is the gap between each value.

For example, if testing temperature, 10 °C, 20 °C, 30 °C, 40 °C and 50 °C gives:

• range: 10 °C to 50 °C
• interval: 10 °C

A wider range may reveal a trend, but intervals must be close enough to show important changes.

Repeats and reliability

A repeat is when you carry out the same measurement again under the same conditions.

Repeats help you identify anomalies and calculate a mean.

If one result is very different from the others, it may be an anomalous result. You should not automatically delete it; you need a valid reason, such as a clear procedural error.

Planning the results and analysis

A strong plan thinks ahead to the data.

Before starting, ask:

• What raw data will be recorded?
• What units are needed?
• Will a mean be calculated?
• Will a rate or percentage change be calculated?
• What graph will best show the relationship?

For many investigations, the independent variable goes in the first column of a results table, and the dependent variable goes in the later columns.

For a graph:

• independent variable usually goes on the x-axis
• dependent variable usually goes on the y-axis
• include units in axis labels
• use a line graph when both variables are continuous
• use a bar chart when the independent variable is categoric

Evaluating whether the method is appropriate

To evaluate a method, you judge whether it can meet the expected outcome. This is not the same as just listing “good” and “bad” points.

A method is appropriate if it:

• tests the stated independent variable
• measures the dependent variable validly
• controls important variables
• uses apparatus with suitable resolution and range
• includes repeats
• produces quantitative data where needed
• allows a clear conclusion to be drawn

Validity

Validity means the investigation actually measures what it claims to measure.

For example, counting bubbles from pondweed can estimate photosynthesis rate, but bubble size may vary. Measuring oxygen volume with a gas syringe or oxygen probe would usually be more valid.

Accuracy and precision in evaluation

A method may be precise but not accurate.

For example, a balance may give repeated readings close together, but if it is not zeroed first, all readings may be inaccurate.

Safety and ethics

Planning also includes safe and responsible practical work.

You may need to consider:

• wearing eye protection when using enzymes, acids or alkalis
• avoiding skin contact with biological stains
• using low-risk microorganisms and aseptic technique where relevant
• disinfecting benches after microbial work
• minimising harm to living organisms
• safely disposing of biological material

You do not need to write a full risk assessment every time, but if a hazard is relevant, include a sensible precaution.

Applying biological knowledge to practical contexts

Planning questions often test whether you can use biology from the specification in a new situation.

For example:

• If enzymes are involved, consider temperature, pH, substrate concentration and enzyme concentration.
• If osmosis is involved, consider water potential, tissue size and surface area.
• If photosynthesis is involved, consider light intensity, carbon dioxide concentration and temperature.
• If microorganisms are involved, consider aseptic technique and incubation conditions.
• If sampling organisms, consider random sampling, sample size and avoiding bias.