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Determine the optimal sample size for statistically significant research results
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Statisticians often consider 30 as the minimum sample size for the Central Limit Theorem to apply reliably.
Most research studies aim for 80% statistical power, meaning an 80% chance of detecting a true effect.
Phase III clinical trials typically require 1,000-3,000 participants to detect treatment effects.
Most national election polls use only 1,000-1,500 respondents to represent millions of voters.
Doubling sample size only reduces margin of error by about 30%, not 50% as you might expect.
The US Census attempts 100% coverage, but most countries rely on samples of 1-5% of the population.
A random sample of 1,000 is often more accurate than a biased sample of 1,000,000.
Detecting small effects (d=0.2) requires 4x more participants than detecting medium effects (d=0.5).
This calculator uses the standard formula for sample size determination with a 50% population proportion (maximum variability). For known proportions or more complex study designs, consult a statistician for precise calculations.