I mean Andrew Fisher's formulae for sample size determination
Hello Hardi,
Andrew Fisher's formula (see link below) is a good starting point for estimating what the total sample size should be (assuming that the selection mechanism is simple random sampling) in order to estimate the value of some population parameter within some target level of precision, with some known risk of the resultant confidence interval completely missing the true value.
If you're intending to stratify the population based on some characteristic (e.g., age, income, geographic location), then sample cases, values from formulae such as A. Fisher's could over-estimate the needed number of cases.
If you're not going to use a probability sampling method (such as simple random sampling), then the formula won't do you any good.
Good luck with your work.
Link: https://www.geopoll.com/blog/sample-size-research/
Fisher's formula for calculating sample size is commonly used for planning studies that involve hypothesis testing, including analytic cross-sectional studies. However, the appropriateness of Fisher's formula for a given study depends on several factors, including the research question, study design, and sampling method.
Fisher's formula is typically used to estimate the minimum sample size required to achieve a desired level of statistical power while controlling for the risk of type I error (false positives). The formula assumes a normal distribution, which is generally reasonable for many analytic cross-sectional studies, particularly if the sample size is large.
However, the formula assumes that the population standard deviation is known, which may not always be the case in practice. If the population standard deviation is unknown, researchers can use an estimate based on prior research or pilot data, but this introduces some uncertainty in the sample size calculation.
Additionally, Fisher's formula assumes a simple random sample, which may not always be feasible or appropriate for analytic cross-sectional studies. In some cases, other sampling methods, such as stratified sampling or cluster sampling, may be more appropriate.
In summary, Fisher's formula can be a useful tool for estimating sample size in analytic cross-sectional studies with a known population, but researchers should consider the assumptions and limitations of the formula and adapt it as needed to ensure an appropriate sample size for their study.
- Badges
- Science topic
- Similar topics
- Computer Science and Engineering
- Bioinformatics