Experimental design — Edexcel A-Level Statistics
Test yourself on Experimental design with PEARSON EDEXCEL A-Level practice questions.
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Experimental design explained
Experimental design aims to isolate the effect of a treatment.
Read the full explanation
Experimental error is the natural variation between units not caused by the treatment; it is background noise, not a mistake. Randomisation allocates units to treatments by a chance mechanism, balancing nuisance factors and avoiding bias. Replication applies each treatment to several independent units, reducing variability and increasing precision. Control and experimental groups allow comparison: the control receives no or standard treatment, while the experimental receives the new treatment. In blind trials, participants do not know their treatment, reducing placebo effects; double blind trials also hide this from assessors, preventing assessment bias.
Your focus
- Explain what experimental error is and why it differs from a mistake or bias.
- Describe how randomisation, replication, control and experimental groups are used in designing an experiment.
- Distinguish blind trials from double blind trials and state the bias each addresses.
Experimental design exam tips
Marking Points
- Experimental error is the inherent variability among experimental units not attributable to the treatment, so it must be reduced or accounted for rather than treated as a mistake.
- Randomisation allocates units to treatments using a chance mechanism, balancing nuisance factors on average and protecting against selection bias.
- Replication applies each treatment to multiple independent units, reducing the standard error of the estimated treatment effect and improving precision.
- A control group provides a baseline (no treatment or standard treatment) and the experimental group receives the treatment under test, so effects are compared against a reference.
- Blinding hides treatment identity from participants to limit placebo and response bias; double blinding also hides it from assessors or analysts to limit assessment bias.
Examiner Tips
- 💡Link each design feature to the specific bias or variability it controls, rather than listing features.
- 💡Use precise vocabulary: distinguish experimental error from bias, and replication from repetition.
- 💡When a scenario is given, identify which feature is present or missing and state the consequence for the conclusions.
Common Mistakes
- Treating experimental error as a blunder by the experimenter; correction: it is the unavoidable random variation between units, distinct from systematic bias.
- Believing that randomisation guarantees perfectly balanced groups; correction: it balances nuisance factors on average, not exactly in every experiment.
- Confusing replication with repeated measurements on the same unit; correction: replication requires several independent units per treatment.
- Thinking blinding removes the need for a control group; correction: blinding reduces bias but comparison still requires a control or baseline.