When does sampling without replacement still count as independent?
In short: In statistics, the assumption of independence is vital for accurate modeling. This video explains the 10% Rule—a practical threshold used to determine when you can treat a sample as independent even when you aren't replacing items after each draw.
The video features Jeff, a former high school teacher and current writer for Khan Academy’s AP Statistics curriculum. It explores the technical nuances of random variables and the specific conditions required to maintain statistical integrity during data collection.
Understanding this rule is essential for students and researchers because it defines the boundary where sampling from a finite population begins to skew results. By following this guideline, analysts can justify using standard formulas even when the pool of subjects is being depleted.
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Sources
- Summary by Intellect · source: 10% Rule of assuming "independence" between trials | Random variables | AP Statistics | Khan Academy
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