Randomization
Random assignment spreads hidden individual differences across groups. With enough participants, those differences roughly cancel out, leaving the treatment as the clearest systematic distinction.
To learn whether a treatment works, Ronald Fisher’s answer was to surrender control over who receives it: assign people by chance. The apparent disorder is precisely what makes the comparison cleaner.
E1Chance neutralizes the differences you cannot measure
People arrive with countless characteristics that could affect an outcome. If treatment and placebo groups are chosen deliberately, those characteristics may cluster unevenly and masquerade as a treatment effect. Random assignment gives each person the same route into either group, distributing individual differences without choosing which ones matter. In a sufficiently large sample, the imbalances tend to cancel on average, so the intervention becomes the clearest systematic difference between the groups. That is the causal core of a Randomized Controlled Trial.
E1Random does not mean automatically balanced
Chance can still produce lopsided groups, especially in a small sample. Randomization reduces systematic allocation bias; it does not guarantee that every relevant difference is evenly distributed in any particular trial.
E1Randomize before the outcome can influence assignment
When comparing two interventions, define the eligible pool first, then use an external random process to assign each case. Record the assignment before observing outcomes, and check whether the resulting groups are large enough for accidental individual differences to plausibly cancel out.
E1Episodes that teach this
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The Randomness of Science - Why Randomised Controlled Trials (RCTs) Work - FutureIQ
· explained at 11:36
4,109 views
Ronald Fisher's idea was to take a large group and randomly assign half to treatment and half to placebo so individual differences cancel out on average.