NDLI: Statistics as viewed by biologists

Content Provider	Springer Nature Link
Author	Boyce, Mark S.
Copyright Year	2002
Abstract	A survey of colleagues and recent periodicals was used to characterize how biologists use statistics, with an emphasis on applications in ecology and evolutionary biology. Biologists use statistics to describe and uncover patterns in data and to ensure that they are obtaining reliable knowledge. Universally, sampling and study design problems plague biological research programs, and this continues to be one of the greatest needs for statistical training. Biologists traditionally have used a Neyman-Pearson approach to hypothesis testing, combined with an overbearing focus on the Fisherian notion of statistical significance and P-values. Multivariate statistics became popular 20 years ago but were used less following the publication of a few critical reviews that questioned the validity of statistical inferences. Advances in nonlinear dynamics that grew from computer technology have stimulated new methods for time-series analysis capable of capturing underlying structures in temporal data. Similarly, computer technology supporting geographical information systems (GIS) has led to widespread application and development of spatial statistics. Recently, Bayesian statistics have found support in a few applications, but it is my impression that most biologists are either suspicious of Bayesian methods, do not understand them, or have a difficult time implementing them. Computer-intensivesimulation methods are commonly used for inference in volving complex models. Biologists are recognizing that statistics have applications beyond hypothesis testing, and we are seeing increased use of likelihood approaches, such as the Akaike information criteria, for model selection. Repeatedly, we find that technological advances in computing power have revolutionized how biologists use statistics.
Starting Page	306
Ending Page	312
Page Count	7
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	7
Issue Number	3
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2002-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Akaike information criteria Bayesian statistics Biological sciences Computerintensive statistics Ecology Evolution Hypothesis testing Model selection Monte Carlo methods Multivariate statistics Sampling Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Bayesian multivariate process modeling for prediction of forest attributes

Bayesian estimation of cluster-level test accuracy based on different sampling schemes

Resampling Unbalanced Ranked Set Samples With Applications in Testing Hypothesis About the Population Mean

Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space

Sequential stopping rules for species accumulation

Sampling Design for Two Combined Samples of the Farm Accountancy Data Network (FADN)

Bootstrap model selection in generalized linear models

Bayesian estimation of survival from mark-recapture data

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Statistics as viewed by biologists

Similar Documents

Bayesian multivariate process modeling for prediction of forest attributes

Bayesian estimation of cluster-level test accuracy based on different sampling schemes

Resampling Unbalanced Ranked Set Samples With Applications in Testing Hypothesis About the Population Mean

Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space

Sequential stopping rules for species accumulation

Sampling Design for Two Combined Samples of the Farm Accountancy Data Network (FADN)

Bootstrap model selection in generalized linear models

Bayesian estimation of survival from mark-recapture data

A Bayesian Approach to Spatial Prediction With Flexible Variogram Models

Statistics as viewed by biologists