Abstract
IF a fraction p of a population have the attribute A, then it is well known that if m members out of a sample of N have this attribute, the best estimate of p is and its standard error is or Supposing, therefore, that we want our estimate of p to be correct within a standard error of 10 per cent of its value, we must count a sample containing 100(1 – p) members with the attribute A. If we do not know p roughly before-hand we do not know how large to take our sample. For example, if we wish to estimate the frequency of a type of blood corpuscle, and count 1,000 blood corpuscles in all, we should get such values as 20 ± 1·3 per cent, or 1 ± 0·31 per cent. The former value would be needlessly precise for many purposes. The latter would not differ significantly from an estimate of 2 per cent.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
HALDANE, J. A Labour-saving Method of Sampling. Nature 155, 49–50 (1945). https://doi.org/10.1038/155049b0
Issue Date:
DOI: https://doi.org/10.1038/155049b0
This article is cited by
-
Errors of Estimation in Inverse Sampling
Nature (1947)
-
Inverse Binomial Sampling
Nature (1945)
-
Inverse Statistical Variates
Nature (1945)
-
Economy in Sampling
Nature (1945)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.