Article written by Adam

13 responses to “Finite Population Correction Factor”

  1. Brad

    Thanks for taking the time to write this article. It’s been a great help. You have managed to simplify the concept in that the z value increases as n gets larger and approaches N, meaning that we can afford greater confidence in our sample or we can lower the sample size and still achive the same level of accuracy.


    I still don’t understand the concept of the fpc ,can you explain further

  3. Aran

    I’ve been looking around on the web trying to find an answer to a question that might relate to the finite correction factor. Let’s say the goal is to estimate a mean of a population of 50. I draw a sample of 25 respondents, and get an average of 100 with a standard dev of 10. If I’m trying to report on the average of the entire population, do I use the FPCF in forming my confidence interval (I get an upper bound of 102.9)? Or do I have another choice to say I’m certain about the 25 being an average of 100, and I have a certain confidence interval about the other 25, so I can calculate an upper bound to my overall confidence intervall by combining the two estimates (25 at 100, and the other 25 at 108 (2 standard errors), for an upper bound of 104?

  4. Akhtar

    more explanation is needed. wil u discuss further pls.

  5. yudi

    hi.. i want to ask, why we use fcf when sample is equal or more than 5% from the population? [please send the answer to my mail.. thx..]

  6. Fiona

    Hi, I got a question from a stats hw. It asks, if an initial survey used 1000 subjects for a population of 304 million, how many subjects would we need for a population of 1.3 billion while retaining the same accuracy? I know this relates to the Finite Sample Correction, but how? please explain!



  7. Pia

    I have seen different formulae for Finite Population Correction Factor (FPCF), such as:

    1. FPCF = (N ā€“ n)/N

    2. FPCF = square root of (N ā€“ n) / (N -1)

    3. FPCF = 1 – n/N, which is the same as #1

    4. nā€™ = n / (1+ n/N), which means the FPCF = N / (N + n). nā€™ is the sample size after taking into account of FPCF.

    Which one should I use?

  8. Kathy

    Your explanation is very helpful – thank you!

    One other question – when is the FPCF ignored?