**Environment statistics** is the application of statistical methods to environmental science. It covers procedures for dealing with questions concerning the natural environment in its undisturbed state, the interaction of humanity with the environment, and urban environments. The field of environmental statistics has seen rapid growth in the past few decades as a response to increasing concern over the environment in the public, organizational, and governmental sectors.

The United Nations' Framework for the Development of Environment Statistics (FDES) defines the scope of environment statistics as follows:^{ [1] } The scope of environment statistics covers biophysical aspects of the environment and those aspects of the socio-economic system that directly influence and interact with the environment. The scope of environment, social and economic statistics overlap. It is not easy – or necessary – to draw a clear line dividing these areas. Social and economic statistics that describe processes or activities with a direct impact on, or direct interaction with, the environment are used widely in environment statistics. They are within the scope of the FDES.

Statistical analysis is essential to the field of environmental sciences, allowing researchers to gain an understanding of environmental issues through researching and developing potential solutions to the issues they study. The applications of statistical methods to environmental sciences are numerous and varied. Environmental statistics are used in many fields including; health and safety organizations, standard bodies, research institutes, water and river authorities, meteorological organizations, fisheries, protection agencies, and in risk, pollution, regulation and control concerns.^{ [2] }

Environmental statistics is especially pertinent and widely used in the academic, governmental, regulatory, technological, and consulting industries.^{ [2] }

Specific applications of statistical analysis within the field of environmental science include earthquake risk analysis, environmental policymaking, ecological sampling planning, environmental forensics.^{ [2] }

Within the scope of environmental statistics, there are two main categories of their uses.^{ [2] }

- Descriptive statistics is not used to make inferences about data, but simply to describe its characteristics.
- Inferential statistics is used to make inferences about data, test hypotheses or make predictions.

Types of studies covered in environmental statistics include:^{ [3] }

- Baseline studies to document the present state of an environment to provide background in case of unknown changes in the future;
- Targeted studies to describe the likely impact of changes being planned or of accidental occurrences;
- Regular monitoring to attempt to detect changes in the environment.

Sources of data for environmental statistics are varied and include surveys related to human populations and the environment, records from agencies managing environmental resources, maps and images, equipment used to examine the environment, and research studies around the world. A primary component of the data is direct observation, although most environmental statistics use a variety of sources.^{ [3] }

Methods of statistical analysis in environmental sciences are as numerous as its applications. While there is a basis for the methods used in other fields, many of these methods must be adapted to suit the needs or limitations of data in environmental science. Linear regression models, generalized linear models, and non-linear models are some methods of statistical analysis that are widely used within environmental science to study relationships between variables.^{ [2] }

**Biostatistics** are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.

**Econometrics** is the application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today.

**Statistics** is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.

**Statistical inference** is the process of using data analysis to infer properties of an underlying distribution of probability. **Inferential statistical analysis** infers properties of a population, for example by **testing hypotheses** and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

**Statistics** is a field of inquiry that studies the collection, analysis, interpretation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities; it is also used and misused for making informed decisions in all areas of business and government.

**Natural capital** is the world's stock of natural resources, which includes geology, soils, air, water and all living organisms. Some natural capital assets provide people with free goods and services, often called ecosystem services. All of these underpin our economy and society, and thus make human life possible.

**Social statistics** is the use of statistical measurement systems to study human behavior in a social environment. This can be accomplished through polling a group of people, evaluating a subset of data obtained about a group of people, or by observation and statistical analysis of a set of data that relates to people and their behaviors.

**Quantitative research** is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.

**Mathematical statistics** is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.

**Calyampudi Radhakrishna Rao**, FRS known as **C. R. Rao** is an Indian-American mathematician and statistician. He is currently professor emeritus at Pennsylvania State University and Research Professor at the University at Buffalo. Rao has been honoured by numerous colloquia, honorary degrees, and festschrifts and was awarded the US National Medal of Science in 2002. The American Statistical Association has described him as "a living legend whose work has influenced not just statistics, but has had far reaching implications for fields as varied as economics, genetics, anthropology, geology, national planning, demography, biometry, and medicine." *The Times of India* listed Rao as one of the top 10 Indian scientists of all time. Rao is also a Senior Policy and Statistics advisor for the Indian Heart Association non-profit focused on raising South Asian cardiovascular disease awareness.

Statistics, in the modern sense of the word, began evolving in the 18th century in response to the novel needs of industrializing sovereign states. The evolution of statistics was, in particular, intimately connected with the development of European states following the peace of Westphalia (1648), and with the development of probability theory, which put statistics on a firm theoretical basis.

**Official statistics** are statistics published by government agencies or other public bodies such as international organizations as a public good. They provide quantitative or qualitative information on all major areas of citizens' lives, such as economic and social development, living conditions, health, education, and the environment.

**David Amiel Freedman** was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models. He published extensively on methods for causal inference and the behavior of standard statistical models under non-standard conditions – for example, how regression models behave when fitted to data from randomized experiments. Freedman also wrote widely on the application—and misapplication—of statistics in the social sciences, including epidemiology, public policy, and law.

Water accounting is a discipline that seeks to provide comprehensive, consistent and comparable policy relevant information related to water. Based on the experience of more than fifty years of national accounts, the discipline that provides the elements to calculate the Gross Domestic Product (GDP), the United Nations Statistics Division (UNSD) developed the **System of Environmental and Economic Accounting for Water** (**SEEA-Water**), which has been adopted by the United Nations Statistical Commission (UNSC) as a statistical interim standard in 2007.

**Data science** is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

The **Committee for the Coordination of Statistical Activities** (**CCSA**) is composed of international and supranational organisations, whose mandate includes the provision of statistics. The CCSA promotes inter-agency coordination and cooperation on statistical programmes and consistency in statistical practices and development. As a forum of committed members, the CCSA fosters good practices in the statistical activities of international and supranational organisations, in accordance with the principles governing international statistical activities. The members of the CCSA contribute actively to the development of a coordinated global statistical system producing and disseminating high-quality statistics.

**Causal inference** is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

- ↑ http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf United Nations Framework for the Development of Environment Statistics
- 1 2 3 4 5 Rong, Yue (2011-09-01). "Environmental Statistics".
*Environmental Forensics*.**12**(3): 189–190. doi:10.1080/15275922.2011.599263. ISSN 1527-5922. - 1 2 Manly B.F.J. (2001)
*Statistics for Environmental Science and Management*, Chapman & Hall/CRC. ISBN 1-58488-029-5

- https://www.oecd-ilibrary.org/environment/data/oecd-environment-statistics_env-data-en
- https://unstats.un.org/unsd/envstats/qindicators.cshtml
- http://www.jenvstat.org/
- https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=137230&Lab=NERL
- https://web.ma.utexas.edu/users/mks/envstat.html
- https://www.umass.edu/landeco/teaching/ecodata/schedule/statistics.pdf
- https://unstats.un.org/unsd/environmentgl/default.asp

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