Observations on the geometrical properties of accuracy growth in sampling with finite populations.
A common problem in sample-based surveys that are performed under operational constraints is how to scale data collection procedures so as to guarantee an acceptable level of sampling efficiency. This problem constitutes a frequent concern of fishery administrations with limited budget and human resources. Generally, lack of any a priori guidance on sample size requirements tends to increase the size and complexity of field operations and this, in turn, has a direct impact on the logistical aspe cts of data collection and data management procedures. The presented study attempts to provide some answers to the above problem and proposes a supplementary practical tool for use in the design of sample-based fishery surveys. Its major assumptions are that the target populations are finite and of known size and that progressively taken samples are random and independent of each other. Its main feature is the limited use of probabilistic approaches and the adoption of geometrical criteria and t echniques that would be described as "reasonably pessimistic". The method is based on a simple definition of a relative index of proximity of a sample mean to the population mean. This index has several geometrical properties that are only a function of the population size. Using these properties it is possible to formulate geometrical constraints that can be used for predicting a lower limit for accuracy at any sample size between 1 and the population size.
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| Format: | Book (series) biblioteca |
| Language: | English |
| Published: |
1999
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| Online Access: | https://openknowledge.fao.org/handle/20.500.14283/X3410E http://www.fao.org/3/a-x3410e.pdf |
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