Planning — OCR A-Level Biology
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Planning explained
Planning covers 3 specification points, including (a) experimental design, including to solve… and (b) identification of variables that must be….
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Open a point below for its explanation and revision guidance.
(a) experimental design, including to solve problems set in a practical context
Experimental design means building a plan that can actually answer a biological question. Start by stating a testable hypothesis and identifying the independent variable you will change, the dependent variable you will measure and how you will measure it with suitable apparatus and units. Then plan controls: a control group or a controlled value for each confounding variable, plus standardisation of procedure. Decide sample size, replication and how you will reduce bias, for example random allocation or random sampling. Think about reliability (repeat readings, means) and validity (does the measurement really test the hypothesis?). Finally, plan how data will be recorded in a suitable table and processed, and consider risks and ethical issues.
(b) identification of variables that must be controlled, where appropriate
In any investigation, some variables are deliberately changed (independent), some are measured (dependent) and the rest must be held constant so they do not distort the result. These controlled variables are the confounding factors. To identify them, ask what else could affect the dependent variable besides the independent variable. For example, in an investigation of enzyme activity against temperature, pH, substrate concentration and enzyme concentration must be controlled because each affects rate. In a plant photosynthesis experiment, light intensity, carbon dioxide concentration and temperature are potential confounders. The phrase 'where appropriate' matters: you control variables that could realistically affect the outcome, and you justify each choice.
(c) evaluation that an experimental method is appropriate to meet the expected outcomes.
Evaluating appropriateness means judging whether a chosen procedure can actually deliver the expected outcome, not merely describing it. Work through a chain: identify the expected outcome (for example, whether catalase concentration affects the rate of hydrogen peroxide breakdown), then ask whether the variables, apparatus, measurements and controls can produce valid, reliable data for that outcome. Consider whether the independent variable is varied over a suitable range, whether the dependent variable is measured with adequate resolution, whether confounding variables are controlled, and whether replication and repeats allow reliability to be judged. Weigh limitations against the outcome: a method may be safe and feasible yet unable to resolve small differences. Conclude with a justified judgement and a realistic improvement, such as finer resolution or a wider range.
Your focus
- Design an investigation that tests a stated hypothesis in a practical context.
- Justify the choice of variables, controls and measurement methods.
- Explain how replication, randomisation and data processing support valid, reliable conclusions.
Show all 9 objectives
- Classify variables in a scenario as independent, dependent or controlled.
- Identify which variables must be held constant and explain why.
- Select the correct option by testing each against its effect on the dependent variable.
- Judge whether a described experimental method can produce valid data for its stated expected outcome.
- Explain how range, resolution, controls and replication each affect the appropriateness of a method.
- Justify an overall verdict on a method and propose an improvement linked to an identified limitation.
Planning flashcards
Card 1 of 10
Independent variable
Planning exam tips
Marking Points
- States a testable hypothesis linking the independent and dependent variables.
- Identifies the independent variable and describes how it will be set or changed, with a suitable range.
- Identifies the dependent variable and describes a valid measurement method with apparatus and units.
- Identifies confounding variables and explains how each will be controlled or standardised.
- Describes a control group or controlled condition and justifies it.
- Explains replication, sample size and randomisation or random sampling to improve reliability and reduce bias.
- Describes how data will be recorded and processed (table, mean, graph) and how a conclusion will be drawn.
- Addresses risk, ethics or practical constraints relevant to the context.
- Distinguishes independent, dependent and controlled variables in a given scenario.
- Identifies a confounding variable that could affect the dependent variable.
- Explains why a named variable must be held constant.
- Selects the option that correctly names a variable requiring control, rejecting distractors that are the independent or dependent variable.
- Identifies the expected outcome of the investigation and states what valid evidence for it would look like.
- Judges whether the independent variable is varied over an appropriate range and with suitable intervals to reveal a trend.
- Evaluates whether the dependent variable is measured with sufficient resolution and an appropriate technique to detect the expected effect.
- Assesses control of confounding variables and whether they are kept constant so the outcome can be attributed to the independent variable.
- Considers replication, repeats and sample size as a basis for judging reliability of the data produced.
- Reaches a justified overall judgement on appropriateness and proposes a specific, realistic improvement linked to a stated limitation.
Examiner Tips
- 💡Read the practical context carefully and apply the design sequence to that specific scenario rather than giving a generic plan.
- 💡Name apparatus and units for the dependent variable so the measurement is clearly valid.
- 💡For each controlled variable, state the method of control, not just the variable name.
- 💡Label each variable in the scenario as independent, dependent or controlled before choosing an option.
- 💡Ask whether each option could affect the dependent variable; if it could and is not being changed, it needs controlling.
- 💡Reject distractors that are the variable being measured or deliberately changed.
- 💡Open by naming the expected outcome so every later judgement has a reference point.
- 💡Use comparative judgement language such as 'sufficient', 'adequate' or 'too coarse', each tied to a specific feature of the method.
- 💡Finish with a clear overall verdict plus one improvement that directly addresses the limitation you identified.
Common Mistakes
- Confusing the independent and dependent variable: the correction is to state clearly which variable the experimenter changes and which is measured as the outcome.
- Listing variables to control without saying how: the correction is to give a specific method of holding each one constant.
- Treating repetition alone as improving validity: the correction is to explain that repetition improves reliability, while validity depends on measuring the right thing and controlling confounders.
- Naming the independent variable as the one to control: the correction is to recognise that the independent variable is deliberately changed, not held constant.
- Assuming every conceivable variable must be controlled: the correction is to control those that could realistically affect the dependent variable and justify the choice.
- Ignoring a confounder that clearly affects the outcome: the correction is to check each option against what could influence the measured result.
- Describing the method step by step instead of judging whether it can meet the expected outcome; correction: frame each point as a judgement about fitness for purpose.
- Treating any safety precaution as evidence of appropriateness; correction: distinguish safety and ethics from validity and reliability of the outcome.
- Claiming a method is appropriate without stating the expected outcome; correction: state the outcome first, then test the method against it.
- Assuming more repeats automatically make results valid; correction: repeats support reliability, while validity depends on controlling variables and measuring the right quantity.