Hello Bodh,
Functionally, once you pass a certain population N (whether verifiable via enumeration or not), the actual size of a population makes no appreciable difference in the requisite sample size needed to estimate some parameter with some target degree of precision with some target confidence level.
Here's a couple of examples (all based on the scenario of estimating the proportion of adults who brush their teeth at least twice daily, within +/- 2%, and with 95% confidence, and using simple random sampling):
Population size : 100,000 Requisite sample (worst case) N : 2345
Population size: 1 million Requisite sample N: 2396
Population size: 5 million Requisite sample N: 2400
Population size: 100 million Requisite sample N: 2401
You can see that the requisite stabilizes to values that are all very close.
Of course, in order to have a probability sample, you'll need to have some way to identify each case (this the the sampling frame, mentioned by Saeed A. Khan ), and that is often the most challenging part of any large-scale sampling study.
Good luck with your work.
By The sample size formula helps us find the accurate sample size through the difference between the population and the sample. To recall, the number of observation in a given sample population is known as sample size. Since it not possible to survey the whole population, we take a sample from the population and then conduct a survey or research.
Hello Bodh,
Functionally, once you pass a certain population N (whether verifiable via enumeration or not), the actual size of a population makes no appreciable difference in the requisite sample size needed to estimate some parameter with some target degree of precision with some target confidence level.
Here's a couple of examples (all based on the scenario of estimating the proportion of adults who brush their teeth at least twice daily, within +/- 2%, and with 95% confidence, and using simple random sampling):
Population size : 100,000 Requisite sample (worst case) N : 2345
Population size: 1 million Requisite sample N: 2396
Population size: 5 million Requisite sample N: 2400
Population size: 100 million Requisite sample N: 2401
You can see that the requisite stabilizes to values that are all very close.
Of course, in order to have a probability sample, you'll need to have some way to identify each case (this the the sampling frame, mentioned by Saeed A. Khan ), and that is often the most challenging part of any large-scale sampling study.
Good luck with your work.
Dear Noor, Salman, Murthy, Khan and Divid Sir
Thanks for insights
Regards
Bodh
Dear Bodh R Sharma
I support the opinion of dear David Morse
Regards, Pushkin Sergey Viktorovich
Bodh R Sharma As it is not possible to take into consideration the entire population which is too large in size, we need to take a sample for the study.
A sample is a small representation of the large whole.
There are various types of sampling in research. Mention may be made of purposive sampling, stratified sampling, random sampling-- each is meant for a particular population group followed by data collection.
You need to select which sampling method suits your work the best .
Thank you for sharing this question
I am following your question
best regards
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