Finite Population Correction Factor
Definition
Finite Population Correction Factor (FPCF) adjusts the standard error of a sample mean or proportion when sampling without replacement from a finite population. It is used to increase the accuracy of statistical estimates.
5 Must Know Facts For Your Next Test
- The FPCF is applied when the sample size is more than 5% of the population size.
- The formula for FPCF is $\sqrt{(N-n)/(N-1)}$, where $N$ is the population size and $n$ is the sample size.
- Using FPCF reduces the standard error, making your confidence intervals narrower and more precise.
- If the population is infinite or if sampling with replacement, FPCF is not needed.
- Applying FPCF can significantly affect results in small populations, leading to more accurate statistical inferences.
Review Questions
- What condition must be met for the Finite Population Correction Factor to be applied?
- What happens to the standard error when you apply the Finite Population Correction Factor?
- Write down and explain the formula for calculating the Finite Population Correction Factor.
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Related terms
Standard Error: A measure of how much sample means would differ from each other if you took multiple samples from the same population.
Central Limit Theorem: States that, given a sufficiently large sample size from a population with a finite level of variance, the sample mean will be approximately normally distributed.
Confidence Interval: A range of values derived from a sample statistic that likely contains the value of an unknown population parameter.
