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Sampling theory considers how methods for selection of a subset of units from a finite population (a sample) affect the accuracy of estimates of descriptive population parameters (mean, total, proportion). Although a sound knowledge of sampling theory principles would seem essential for ecologists and natural resource scientists, the subject tends to be somewhat overlooked in contrast to other core statistical topics such as regression analysis, experimental design, and multivariate statistics. This introductory text aims to redress this imbalance by specifically targeting ecologists and resource scientists, and illustrating how sampling theory can be applied in a wide variety of resource contexts. The emphasis throughout is on design-based sampling from finite populations, but some attention is given to model-based prediction and sampling from infinite populations. Sampling Theory is an introductory textbook suitable for advanced undergraduates, graduate students, professional researchers, and practitioners in the fields of ecology, evolution, conservation biology, and natural resource sciences (including fisheries, wildlife, rangeland, ecology and forestry).
This text investigates how the selection of subsets from finite populations influences the accuracy of statistical estimates within ecological and natural resource management contexts. The authors, David G. Hankin, Kenneth B. Newman, and Michael S. Mohr, provide a structured pedagogical framework designed to bridge the gap between theoretical sampling principles and practical field application. By focusing on design-based sampling, the book addresses the frequent oversight of these methods in standard statistical curricula for environmental scientists.
What You Will Find
Scope Limits
Experts identify this work as a foundational resource for bridging the gap between abstract statistical theory and the specific requirements of field-based ecological research. Readers frequently note the technical clarity of the prose, which makes complex sampling methodologies accessible to both advanced students and practicing professionals in the natural sciences.
Page Count:
368
Publication Date:
2019-01-01
Publisher:
OUP Oxford
ISBN-10:
0192547844
ISBN-13:
9780192547842
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