Fair experiments and random assignment — Data, 14–17 years
A well-designed experiment separates the effect of a treatment from other differences between the groups.
What makes an experiment fair
In an experiment, a researcher changes one factor and compares outcomes. A control group provides a useful comparison, while random assignment gives people a fair chance of entering each group. Keeping other conditions similar makes a difference in results more plausibly linked to the treatment.
Why random assignment was needed
If volunteers choose the new treatment, they may already be more motivated, healthier or more confident than others. Then an improvement could come from those differences rather than the treatment. Random assignment was developed to reduce this mixing of causes, so a comparison can answer a clearer question: did the treatment make a difference?
A worked example
A school tests a revision app with 40 students. First, number the students from 1 to 40. Second, use a random method to put 20 in the app group and 20 in the usual-study group. Third, give both groups the same test. If app students score 74 on average and the others 68, the difference is 6 points, not proof by itself, but a fair comparison.
The trap: random does not mean careless
People sometimes think that picking names from a hat guarantees a perfect experiment. Random assignment balances groups on average, but small groups can still differ by chance. The idea is reasonable because randomness removes personal choice; it reduces unfair differences, but it does not remove every source of uncertainty.
Where it is used
Clinical trials use treatment and control groups to compare medicines, and product teams may test two versions of a website. In both cases, random assignment helps separate the effect of the change from differences between users. Ethical rules still matter: no design makes an unsafe or unfair study acceptable.
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