Randomisation in experiments is the deliberate use of chance to allocate samples, organisms or participants to conditions. The core aim of Science mastery is not to make experiments feel arbitrary. It is to reduce the chance that one group begins systematically different from another in a way that could distort the result.
For students and parents searching for randomisation in experiments, random assignment, random sampling vs random assignment, randomised experiment, experimental groups or how randomisation reduces bias, the key idea is: random assignment helps distribute known and unknown differences across groups so the treatment becomes the main planned difference.
Randomisation protects comparisons before the experiment begins.
The 60-Second Randomisation Idea
Random assignment means each eligible experimental unit has a defined chance of being placed into each condition.
Examples:
- drawing labelled numbers;
- using a random-number generator;
- using computer randomisation;
- shuffling coded samples.
The exact method depends on the study.
Wait, What? Random Assignment Is Not the Same as Random Sampling?
Correct.
Random sampling: how units are selected from the population.
Random assignment: how selected units are allocated to experimental conditions.
Random sampling helps represent the population.
Random assignment helps create comparable experimental groups.
One study can use one, both or neither.
Why Random Assignment Matters
Suppose the strongest plants are deliberately placed in the fertiliser group.
If those plants grow more, the treatment effect is confounded with starting quality.
Random assignment reduces the chance of systematic selection like this.
It does not guarantee perfectly identical groups, especially with small samples, but it improves fairness.
A Worked Example: Plant Growth
Thirty similar seedlings are available.
Instead of choosing which plants receive fertiliser by appearance, assign each seedling randomly to:
- fertiliser group;
- control group.
Now starting differences are less likely to be deliberately concentrated in one group.
A Worked Example: Reaction-Time Study
Participants are assigned randomly to:
- quiet environment;
- background-noise environment.
Randomisation helps reduce systematic differences in prior reaction speed, age or other characteristics between groups.
Important variables may still need to be measured or controlled.
Randomisation and Confounding
Random assignment is one tool for reducing confounding.
If many relevant characteristics are distributed roughly across groups, the treatment is easier to isolate.
Randomisation and Bias
Randomisation can reduce selection bias in group allocation.
It does not eliminate:
- measurement bias;
- observer bias;
- publication bias;
- poor sampling of the wider population.
See Bias in Science.
Randomisation and Control Groups
A strong experiment may include:
- random assignment;
- experimental group;
- control group;
- consistent measurement;
- relevant control variables.
These features work together.
See Control Group.
Randomisation and Blinding
Randomisation decides group allocation.
Blinding reduces the chance that participants or observers know which condition applies.
They solve different problems.
Randomisation reduces systematic group differences.
Blinding reduces expectation-related bias.
Why Small Samples Can Still Be Imbalanced
Random does not mean perfectly equal.
With only six participants, chance might place more high-performing individuals in one group.
Larger samples usually make extreme imbalances less likely, though balance is never guaranteed.
See Sample Size.
Stratified or Block Randomisation
At more advanced levels, researchers may first group units by an important characteristic, then randomise within those blocks.
For example:
- age group;
- starting score;
- sex where scientifically relevant;
- site;
- baseline disease severity.
This can improve balance on important variables.
Randomisation in Field Experiments
Randomisation can also be used with:
- plots of land;
- water samples;
- containers;
- experimental runs.
For example, treatments may be randomly assigned to field plots to reduce location bias.
Randomisation and Causation
Randomised controlled experiments are powerful for causal inference because:
- the treatment is deliberately assigned;
- groups are made more comparable;
- important alternative explanations are reduced.
This strengthens causal interpretation compared with simple observational correlation.
Primary Science Randomisation
Primary learners can use simple versions:
- draw names from a container;
- shuffle labelled cards;
- randomly choose sample locations.
The goal is to learn that choice method can affect fairness.
Secondary Science Randomisation
Secondary students should increasingly distinguish:
- random sampling;
- random assignment;
- control groups;
- confounding;
- blinding;
- selection bias.
How to Practise Randomisation Reasoning
For any experiment, ask:
- What are the experimental units?
- How are they assigned to conditions?
- Could the researcher’s choices favour one group?
- Would randomisation help?
- What other controls are still needed?
Common Randomisation Mistakes
- confusing random sampling with random assignment;
- assuming random means perfectly balanced;
- thinking randomisation fixes poor measurement;
- assuming random allocation removes every bias;
- calling convenience assignment random;
- ignoring sample size.
Frequently Asked Questions
What is randomisation in an experiment?
Randomisation is the use of a chance process to assign experimental units to conditions.
Why is random assignment useful?
It helps reduce systematic differences between groups and therefore reduces confounding.
Is random assignment the same as random sampling?
No. Random sampling selects from a population; random assignment allocates selected units to conditions.
Does randomisation remove all bias?
No. Measurement, observer, sampling and reporting biases can still remain.
Useful eduKateSG Routes
The Core Aim
Randomisation protects an experiment from hidden unfairness in group assignment.
Let chance allocate the conditions. Then keep measurement and controls strong.
That is the core aim: reduce systematic group differences before they become competing explanations.
Properly taught kids shine a bright light into the future.