In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit; some items are more to generate a nonresponse than others: for example items about private subjects such as income. Attrition is a type of missingness that can occur in longitudinal studies—for instance studying development where a measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing. Data are missing in research in economics and political science because governments or private entities choose not to, or fail to, report critical statistics, or because the information is not available. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry.
These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing at random, missing at random, missing not at random. Missing data can be handled as censored data. Understanding the reasons why data are missing is important for handling the remaining data correctly. If values are missing at random, the data sample is still representative of the population, but if the values are missing systematically, analysis may be biased. For example, in a study of the relation between IQ and income, if participants with an above-average IQ tend to skip the question ‘What is your salary?’, analyses that do not take into account this missing at random may falsely fail to find a positive association between IQ and salary. Because of these problems, methodologists advise researchers to design studies to minimize the occurrence of missing values. Graphical models can be used to describe the missing data mechanism in detail. Values in a data set are missing at random if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, occur at random.
When data are MCAR, the analysis performed on the data is unbiased. In the case of MCAR, the missingness of data is unrelated to any study variable: thus, the participants with observed data are in effect a random sample of all the participants assigned a particular intervention. With MCAR, the random assignment of treatments is assumed to be preserved, but, an unrealistically strong assumption in practice. Missing at random occurs when the missingness is not random, but where missingness can be accounted for by variables where there is complete information. Since MAR is an assumption, impossible to verify statistically, we must rely on its substantive reasonableness. An example is that males are less to fill in a depression survey but this has nothing to do with their level of depression, after accounting for maleness. Depending on the analysis method, these data can still induce parameter bias in analyses due to the contingent emptiness of cells. However, if the parameter is estimated with Full Information Maximum Likelihood, MAR will provide asymptotically unbiased estimates.
Missing not at random is data, neither MAR nor MCAR. To extend the previous example, this would occur if men failed to fill in a depression survey because of their level of depression. Missing data reduces the representativeness of the sample and can therefore distort inferences about the population. Speaking, there are three main approaches to handle missing data: Imputation—where values are filled in the place of missing data, omission—where samples with invalid data are discarded from further analysis and analysis—by directly applying methods unaffected by the missing values. In some practical application, the experimenters can control the level of missingness, prevent missing values before gathering the data. For example, in computer questionnaires, it is not possible to skip a question. A question has to be answered, otherwise one cannot continue to the next. So missing values due to the participant are eliminated by this type of questionnaire, though this method may not be permitted by an ethics board overseeing the research.
In survey research, it is common to make multiple efforts to contact each individual in the sample sending letters to attempt to persuade those who have decided not to participate to change their minds. However, such techniques can either help or hurt in terms of reducing the negative inferential effects of missing data, because the kind of people who are willing to be persuaded to participate after refusing or not being home are to be different from the kinds of people who will still refuse or remain unreachable after additional effort. In situations where missing values are to occur, the researcher is advised on planning to use methods of data analysis methods that are robust to missingness. An analysis is robust when we are confident that mild to moderate violations of the technique's key assumptions will produce little or no bias, or distortion in the conclusions drawn about the population; some data analysis techniques are not robust to missingness, require to "fill in", or impute the missing data.
Rubin argued that re
Sir Ronald Aylmer Fisher was a British statistician and geneticist. For his work in statistics, he has been described as "a genius who single-handedly created the foundations for modern statistical science" and "the single most important figure in 20th century statistics". In genetics, his work used mathematics to combine natural selection. For his contributions to biology, Fisher has been called "the greatest of Darwin’s successors". From 1919 onward, he worked at the Rothamsted Experimental Station for 14 years, he established his reputation there in the following years as a biostatistician. He is known as one of the three principal founders of population genetics, he outlined Fisher's principle, the Fisherian runaway and sexy son hypothesis theories of sexual selection. His contributions to statistics include the maximum likelihood, fiducial inference, the derivation of various sampling distributions, founding principles of the design of experiments, much more. Fisher held strong views on race.
Throughout his life, he was a prominent supporter of eugenics, an interest which led to his work on statistics and genetics. Notably, he was a dissenting voice in UNESCO's statement The Race Question, insisting on racial differences. Fisher was born in East Finchley in London, into a middle-class household, he was one of twins, with the other twin being still-born and grew up the youngest, with three sisters and one brother. From 1896 until 1904 they lived at Inverforth House in London, where English Heritage installed a blue plaque in 2002, before moving to Streatham, his mother, died from acute peritonitis when he was 14, his father lost his business 18 months later. Lifelong poor eyesight caused his rejection by the British Army for World War I, but developed his ability to visualize problems in geometrical terms, not in writing mathematical solutions, or proofs, he entered Harrow School won the school's Neeld Medal in mathematics. In 1909, he won a scholarship to study Mathematics at Cambridge.
In 1912, he gained a First in Astronomy. In 1915 he published a paper The evolution of sexual preference on sexual mate choice. During 1913–1919, Fisher worked for six years as a statistician in the City of London and taught physics and maths at a sequence of public schools, at the Thames Nautical Training College, at Bradfield College. There he settled with Eileen Guinness, with whom he had two sons and six daughters. In 1918 he published "The Correlation Between Relatives on the Supposition of Mendelian Inheritance", in which he introduced the term variance and proposed its formal analysis, he put forward a genetics conceptual model showing that continuous variation amongst phenotypic traits measured by biostatisticians could be produced by the combined action of many discrete genes and thus be the result of Mendelian inheritance. This was the first step towards establishing population genetics and quantitative genetics, which demonstrated that natural selection could change allele frequencies in a population, resulting in reconciling its discontinuous nature with gradual evolution.
Joan Box, Fisher's biographer and daughter says that Fisher had resolved this problem in 1911. In 1919, he began working at the Rothamsted Experimental Station for 14 years, where he analysed its immense data from crop experiments since the 1840s, developed the analysis of variance. In 1919, he was offered a position at the Galton Laboratory in University College London led by Karl Pearson, but instead accepted a temporary job at Rothamsted in Harpenden to investigate the possibility of analysing the vast amount of crop data accumulated since 1842 from the "Classical Field Experiments", he analysed the data recorded over many years and in 1921, published Studies in Crop Variation, his first application of the analysis of variance ANOVA. In 1928, Joseph Oscar Irwin began a three-year stint at Rothamsted and became one of the first people to master Fisher's innovations. Between 1912 and 1922 Fisher recommended and vastly popularized Maximum likelihood. Fisher's 1924 article On a distribution yielding the error functions of several well known statistics presented Pearson's chi-squared test and William Gosset's Student's t-distribution in the same framework as the Gaussian distribution and is where he developed Fisher's z-distribution a new statistical method used decades as the F distribution.
He pioneered the principles of the design of experiments and the statistics of small samples and the analysis of real data. In 1925 he published Statistical Methods for Research Workers, one of the 20th century's most influential books on statistical methods. Fisher's method is a technique for data fusion or "meta-analysis"; this book popularized the p-value, plays a central role in his approach. Fisher proposes the level p=0.05, or a 1 in 20 chance of being exceeded by chance, as a limit for statistical significance, applies this to a normal distribution, thus yielding the rule of two standard deviations for statistical significance. The 1.96, the approximate value of the 97.5 percentile point of the normal distribution used in probability and statistics originated in this book. "The value for which P=.05, or 1 in 20, is 1.96 or nearly 2
Statistics is a branch of mathematics dealing with data collection, analysis and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics 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. See glossary of probability and statistics; when census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, inferential statistics, which draw conclusions from data that are subject to random variation. Descriptive statistics are most concerned with two sets of properties of a distribution: central tendency seeks to characterize the distribution's central or typical value, while dispersion characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets.
Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors and Type II errors. Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis. Measurement processes that generate statistical data are subject to error. Many of these errors are classified as random or systematic, but other types of errors can be important; the presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems. Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more from calculus and probability theory. In more recent years statistics has relied more on statistical software to produce tests such as descriptive analysis.
Some definitions are: Merriam-Webster dictionary defines statistics as "a branch of mathematics dealing with the collection, analysis and presentation of masses of numerical data." Statistician Arthur Lyon Bowley defines statistics as "Numerical statements of facts in any department of inquiry placed in relation to each other."Statistics is a mathematical body of science that pertains to the collection, interpretation or explanation, presentation of data, or as a branch of mathematics. Some consider statistics to be a distinct mathematical science rather than a branch of mathematics. While many scientific investigations make use of data, statistics is concerned with the use of data in the context of uncertainty and decision making in the face of uncertainty. Mathematical statistics is the application of mathematics to statistics. Mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, measure-theoretic probability theory.
In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Ideally, statisticians compile data about the entire population; this may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types, while frequency and percentage are more useful in terms of describing categorical data; when a census is not feasible, a chosen subset of the population called. Once a sample, representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are due to uncertainty.
To still draw meaningful conclusions about the entire population, in
Open data is the idea that some data should be available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control. The goals of the open-source data movement are similar to those of other "open" movements such as open-source software, open content, open education, open educational resources, open government, open knowledge, open access, open science, the open web. Paradoxically, the growth of the open data movement is paralleled by a rise in intellectual property rights; the philosophy behind open data has been long established, but the term "open data" itself is recent, gaining popularity with the rise of the Internet and World Wide Web and with the launch of open-data government initiatives such as Data.gov, Data.gov.uk and Data.gov.in. Open data, can be linked data. One of the most important forms of open data is open government data, a form of open data created by ruling government institutions. Open government data's importance is borne from it being a part of citizens' everyday lives, down to the most routine/mundane tasks that are far removed from government.
The concept of open data is not new. One definition is the Open Definition which can be summarized in the statement that "A piece of data is open if anyone is free to use and redistribute it – subject only, at most, to the requirement to attribute and/or share-alike." Other definitions, including the Open Data Institute's "Open data is data that anyone can access, use or share", have an accessible short version of the definition but refer to the formal definition. Open data may include non-textual material such as maps, connectomes, chemical compounds and scientific formulae, medical data and practice and biodiversity. Problems arise because these are commercially valuable or can be aggregated into works of value. Access to, or re-use of, the data is controlled by organisations, both private. Control may be through access restrictions, copyright and charges for access or re-use. Advocates of open data argue that these restrictions are against the common good and that these data should be made available without restriction or fee.
In addition, it is important that the data are re-usable without requiring further permission, though the types of re-use may be controlled by a license. A typical depiction of the need for open data: Numerous scientists have pointed out the irony that right at the historical moment when we have the technologies to permit worldwide availability and distributed process of scientific data, broadening collaboration and accelerating the pace and depth of discovery... we are busy locking up that data and preventing the use of correspondingly advanced technologies on knowledge. Creators of data do not consider the need to state the conditions of ownership, licensing and re-use. For example, many scientists do not regard the published data arising from their work to be theirs to control and consider the act of publication in a journal to be an implicit release of data into the commons. However, the lack of a license makes it difficult to determine the status of a data set and may restrict the use of data offered in an "Open" spirit.
Because of this uncertainty it is possible for public or private organizations to aggregate said data, protect it with copyright and resell it. The issue of indigenous knowledge poses a great challenge in terms of capturing and distribution. Many societies in third-world countries lack the technicality processes of managing the IK. At his presentation at the XML 2005 conference, Connolly displayed these two quotations regarding open data: "I want my data back." "I've long believed that customers of any application own the data they enter into it." Open data can come from any source. This section lists some of the fields; the concept of open access to scientific data was institutionally established with the formation of the World Data Center system, in preparation for the International Geophysical Year of 1957–1958. The International Council of Scientific Unions oversees several World Data Centres with the mandate to minimize the risk of data loss and to maximize data accessibility. While the open-science-data movement long predates the Internet, the availability of fast, ubiquitous networking has changed the context of Open science data, since publishing or obtaining data has become much less expensive and time-consuming.
The Human Genome Project was a major initiative. It was built upon the so-called Bermuda Principles, stipulating that: "All human genomic sequence information should be available and in the public domain in order to encourage research and development and to maximise its benefit to society'. More recent initiatives such as the Structural Genomics Consortium have illustrated that the open data approach can be used productively within the context of industrial R&D. In 2004, the Science Ministers of all nations of the Organisation for Economic Co-operation and Development, which includes most developed countries of the world, signed a declaration which states that all publicly funded archive data should be made publicly available. Following a request and an intense discussion with data-pr
In a relational database, a column is a set of data values of a particular simple type, one value for each row of the database. A column may contain text values, numbers, or pointers to files in the operating system; some relational database systems allow columns to contain more complex data types. A column can be called an attribute; each row would provide a data value for each column and would be understood as a single structured data value. For example, a database that represents company contact information might have the following columns: ID, Company Name, Address Line 1, Address Line 2, Postal Code. More formally, each row can be interpreted as a relvar, composed of a set of tuples, with each tuple consisting of the relevant column and its value, for example, the tuple; the word'field' is used interchangeably with'column'. However, database perfectionists tend to favor using'field' to signify a specific cell of a given row. Relational databases use row-based data storage, but column-based storage can be more useful for many business applications.
For example, a column database has faster access to which columns can read throughout the ranging process of a query. Any of the columns are known to serve as an index. Alternatively, row-based applications process only one record at one time and need to access a complete record or two. Column databases have better compression as the data storage permits effective compression since the majority of the columns cover only a few distinct values compared to the number of rows. Furthermore, in a column store, data is vertically divided; this vertical organization allows operations on different columns to be processed in parallel. If multiple items need to be searched or aggregated, each of these operations can be assigned to a different processor core. In a row-based database table, rows are read through and checked when retrieving data representing the desired columns. Therefore, requests on a large amount of data can take a lot of time, whereas, in column database tables, this information is kept physically next to each other, knowingly increasing the speed of certain data queries.
The main benefit of keeping data in a column database is that some queries can come quickly. For instance, if you want to know the average age of all users, you can jump to the area where the'age' data is stored and read just the data needed instead of searching up the age for each record row by row. During querying, columnar storage avoids going over non-relevant data. Therefore, aggregation queries where one only needs to look up subsets of total data develop more compared to row-oriented databases; as the data type of each column is alike, better compression occurs when running compression algorithms on each column, which will help queries churn results more quickly. There are many situations. Column databases are not the best option for these types of queries; the more fields that need reading per record, the fewer benefits there are in storing data in a column-oriented fashion. If queries are looking for user-specific values only, row-oriented databases perform those queries faster. Secondly, writing new data could take more time in columnar storage.
For instance, if you're inserting a new record into a row-oriented database, you can write that in one process. However, if you're inserting a new record into a column database, you need to write to each column one by one; this results as it will take longer time when loading new data or updating many values in a columnar database. Some examples of popular databases include: Sybase DB2 MySQL SQL Server Access Oracle PostgreSQL Column-oriented DBMS, optimization for column-centric queries Column, a similar object used in distributed data stores Row SQL Query language Structured Query Language
United Nations Office for the Coordination of Humanitarian Affairs
The United Nations Office for the Coordination of Humanitarian Affairs is a United Nations body formed in December 1991 by General Assembly Resolution 46/182. The resolution was designed to strengthen the UN's response to complex emergencies and natural disasters. Earlier UN organizations with similar tasks were the Department of Humanitarian Affairs, its predecessor, the Office of the United Nations Disaster Relief Coordinator. In 1998, due to reorganization, DHA merged into OCHA and was designed to be the UN focal point on major disasters, it is a sitting observer of the political debate United Nations Development Group. After merging with the DHA, its mandate was expanded to encompass the coordination of humanitarian response, policy development and humanitarian advocacy; the agency's activities include organization and monitoring of humanitarian funding, as well as information exchange and rapid-response teams for emergency relief. Since May 2017, OCHA is led by Mark Lowcock as Under-Secretary-General for Humanitarian Affairs and Emergency Relief Coordinator, appointed for a five-year term.
From 2013 to 2016, OCHA organized the World Humanitarian Summit, held in Istanbul, Turkey, on May 23 and 24, 2016. OCHA is headed by the Under-Secretary-General for Humanitarian Affairs and Emergency Relief Coordinator, since May 2017 by Mark Lowcock, it has 2 headquarters in New York and Geneva, 8 regional offices, 32 field offices, 23 humanitarian adviser teams, 3 liaison offices. As of June 2016, OCHA has 2,300 staff spread across the world in over 60 countries. Major OCHA country offices are located in all continents, among others in Afghanistan, Central African Republic, Colombia, Democratic Republic of Congo, Ivory Coast, Palestinian territories, Sri Lanka, Sudan and Zimbabwe, while regional offices are located in Panama City, Cairo and Bangkok. OCHA has some liaison and support staff in New York and Geneva. OCHA has built up a range of services in the execution of its mandate; some of the larger ones are: IRIN, Integrated Regional Information Networks, a humanitarian news and analysis service Since 1 January 2015, IRIN now operates as an independent news service and is no longer affiliated with OCHA.
INSARAG, International Search and Rescue Advisory Group ReliefWeb, a leading source of time-critical humanitarian information on global crises and disasters. ReliefWeb is a 24/7 service that provides the latest reports, maps and videos from trusted sources, as well as jobs and training programs for humanitarians. Central Emergency Response Fund, a humanitarian fund established by the UN General Assembly to 1) promote early action and response to reduce loss of life. Who does What Where Database and Contact Management Directory: To ensure that appropriate and timely humanitarian response is delivered during a disaster or emergency, information must be managed efficiently; the key information that are important to assess and ensure that humanitarian needs are met in any emergency/disaster are, to know which organizations are carrying out what activities in which locations, universally referred to as the 3W. The integrated Contact Management Directory, complements the 3W database, making it easy for the user to navigate through the application.
Common and Fundamental Operational Datasets are critical datasets that are used to support the work of humanitarian actors across multiple sectors. They are considered a de facto standard for the humanitarian community and should represent the best-available datasets for each theme; the Fundamental Operational Datasets are datasets that are relevant to a humanitarian operation, but are more specific to a particular sector or otherwise do not fit into one of the seven COD themes. Since 2004, OCHA has partnered with the Center for Excellence in Disaster Management and Humanitarian Assistance to facilitate OCHA's Civil Military Coordination course in the Asia-Pacific Region; the UN-CMCoord Course is designed to address the need for coordination between international civilian humanitarian actors UN humanitarian agencies, international military forces in an international humanitarian emergency. This established UN training plays a critical role in building capacity to facilitate effective coordination in the field by bringing together 30 practitioners from the spectrum of actors sharing operational space during a humanitarian crisis and training them on UN coordination mechanisms and internationally recognized guidelines for civil military coordination.
Office for the Coordination of Humanitarian Affairs occupied Palestinian territory. OCHA's Country Office in the occupied Palestinian territory, established in 2002 to support international efforts to respond to the deteriorating humanitarian situation in the oPt; the OCHA encourages humanitarian innovation within organizations. For organizations, it is a way of identifying and solving problems while changing business models to adapt to new opportunities. In OCHA's occasional policy paper Humanitarian Innovation: The State of the Art, they list the reasons why organizations are moving toward providing their own kind of humanitarian service through innovation: Shifting business models based on public demand: There is a growing amount of humanitarian emergencies and the old model of respons
In statistics, quality assurance, survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question. Two advantages of sampling are lower cost and faster data collection than measuring the entire population; each observation measures one or more properties of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications. Successful statistical practice is based on focused problem definition. In sampling, this includes defining the "population".
A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample of that population. Sometimes what defines. For example, a manufacturer needs to decide whether a batch of material from production is of high enough quality to be released to the customer, or should be sentenced for scrap or rework due to poor quality. In this case, the batch is the population. Although the population of interest consists of physical objects, sometimes it is necessary to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or a study on endangered penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on discrete occasions.
In other cases, the examined'population' may be less tangible. For example, Joseph Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo, used this to identify a biased wheel. In this case, the'population' Jagger wanted to investigate was the overall behaviour of the wheel, while his'sample' was formed from observed results from that wheel. Similar considerations arise when taking repeated measurements of some physical characteristic such as the electrical conductivity of copper; this situation arises when seeking knowledge about the cause system of which the observed population is an outcome. In such cases, sampling theory may treat the observed population as a sample from a larger'superpopulation'. For example, a researcher might study the success rate of a new'quit smoking' program on a test group of 100 patients, in order to predict the effects of the program if it were made available nationwide. Here the superpopulation is "everybody in the country, given access to this treatment" – a group which does not yet exist, since the program isn't yet available to all.
Note that the population from which the sample is drawn may not be the same as the population about which information is desired. There is large but not complete overlap between these two groups due to frame issues etc.. Sometimes they may be separate – for instance, one might study rats in order to get a better understanding of human health, or one might study records from people born in 2008 in order to make predictions about people born in 2009. Time spent in making the sampled population and population of concern precise is well spent, because it raises many issues and questions that would otherwise have been overlooked at this stage. In the most straightforward case, such as the sampling of a batch of material from production, it would be most desirable to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible or practical. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will vote at a forthcoming election.
These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory. As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample; the most straightforward type of frame is a list of elements of the population with appropriate contact information. For example, in an opinion poll, possible sampling frames include an electoral register and a telephone directory. A probability sample is a sample in which every unit in the population has a chance of being selected in the sample, this probability can be determined; the combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection. Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, randomly select one adult from each household..
We interview the selected person and find their income