876:) is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping center early in the morning on a given day, the people that they could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include:
1290:), or survey administrators may not have been able to contact them. In this case, there is a risk of differences between respondents and nonrespondents, leading to biased estimates of population parameters. This is often addressed by improving survey design, offering incentives, and conducting follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame. The effects can also be mitigated by weighting the data (when population benchmarks are available) or by imputing data based on answers to other questions. Nonresponse is particularly a problem in internet sampling. Reasons for this problem may include improperly designed surveys, over-surveying (or survey fatigue), and the fact that potential participants may have multiple e-mail addresses, which they do not use anymore or do not check regularly.
795:. This is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster (rather than using all units contained in all selected clusters). In following stages, in each of those selected clusters, additional samples of units are selected, and so on. All ultimate units (individuals, for instance) selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random subsamples of preceding random samples.
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criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata. Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods (although in most cases, the required sample size would be no larger than would be required for simple random sampling).
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second school 151 to 330 (= 150 + 180), the third school 331 to 530, and so on to the last school (1011 to 1500). We then generate a random start between 1 and 500 (equal to 1500/3) and count through the school populations by multiples of 500. If our random start was 137, we would select the schools which have been allocated numbers 137, 637, and 1137, i.e. the first, fourth, and sixth schools.
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variable during the sampling phase. Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, poststratification can be used to implement weighting, which can improve the precision of a sample's estimates.
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vice versa), leading to an unrepresentative sample. Selecting (e.g.) every 10th street number along the street ensures that the sample is spread evenly along the length of the street, representing all of these districts. (If we always start at house #1 and end at #991, the sample is slightly biased towards the low end; by randomly selecting the start between #1 and #10, this bias is eliminated.)
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not usually 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 (in advance of the election). These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.
198:" from which our sample is drawn. A population can be defined as including all people or items with the characteristics one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
1106:('WR' â an element may appear multiple times in the one sample). For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water or
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the research and link to a survey. After following the link and completing the survey, the volunteer submits the data to be included in the sample population. This method can reach a global population but is limited by the campaign budget. Volunteers outside the invited population may also be included in the sample.
832:. For example, interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for several years.
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for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates.
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In many situations, the sample fraction may be varied by stratum and data will have to be weighted to correctly represent the population. Thus for example, a simple random sample of individuals in the United
Kingdom might not include some in remote Scottish islands who would be inordinately expensive
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Sampling enables the selection of right data points from within the larger data set to estimate the characteristics of the whole population. For example, there are about 600 million tweets produced every day. It is not necessary to look at all of them to determine the topics that are discussed during
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The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available â
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Example: Suppose we have six schools with populations of 150, 180, 200, 220, 260, and 490 students respectively (total 1500 students), and we want to use student population as the basis for a PPS sample of size three. To do this, we could allocate the first school numbers 1 to 150, the
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In some cases the sample designer has access to an "auxiliary variable" or "size measure", believed to be correlated to the variable of interest, for each element in the population. These data can be used to improve accuracy in sample design. One option is to use the auxiliary variable as a basis for
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When the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. The ratio of the size of this random selection
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of elements has the same chance of selection as any other such pair (and similarly for triples, and so on). This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which
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People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income
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The population from which the sample is drawn may not be the same as the population from which information is desired. Often there is a large but not complete overlap between these two groups due to frame issues etc. (see below). Sometimes they may be entirely separate â for instance, one might study
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Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed (before other sampling methods could be applied). By eliminating the work involved in describing clusters that are not selected, multistage sampling can reduce the large costs
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Third, it is sometimes the case that data are more readily available for individual, pre-existing strata within a population than for the overall population; in such cases, using a stratified sampling approach may be more convenient than aggregating data across groups (though this may potentially be
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Second, utilizing a stratified sampling method can lead to more efficient statistical estimates (provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples). Even if a stratified sampling approach does not lead to increased statistical
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Also, simple random sampling can be cumbersome and tedious when sampling from a large target population. In some cases, investigators are interested in research questions specific to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a
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sampling-method allows estimates of changes in the population, for example with regard to chronic illness to job stress to weekly food expenditures. Panel sampling can also be used to inform researchers about within-person health changes due to age or to help explain changes in continuous dependent
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Volunteers may be invited through advertisements in social media. The target population for advertisements can be selected by characteristics like location, age, sex, income, occupation, education, or interests using tools provided by the social medium. The advertisement may include a message about
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Cluster sampling (also known as clustered sampling) generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between one another as compared to the within-cluster variation. For this reason, cluster sampling requires a larger
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listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters. In the example above, the sample only requires a block-level city map for initial selections, and then a household-level map
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Systematic sampling theory can be used to create a probability proportionate to size sample. This is done by treating each count within the size variable as a single sampling unit. Samples are then identified by selecting at even intervals among these counts within the size variable. This method is
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In the most straightforward case, such as the sampling of a batch of material from production (acceptance sampling by lots), 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
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Although the population of interest often 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
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to estimate characteristics of the whole population. The subset is meant to reflect the whole population and statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population,
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More generally, data should usually be weighted if the sample design does not give each individual an equal chance of being selected. For instance, when households have equal selection probabilities but one person is interviewed from within each household, this gives people from large households a
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For example, suppose we wish to sample people from a long street that starts in a poor area (house No. 1) and ends in an expensive district (house No. 1000). A simple random selection of addresses from this street could easily end up with too many from the high end and too few from the low end (or
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chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection cannot be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection.
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is the method of first selecting a group of participants through a random sampling method and then asking that group for (potentially the same) information several times over a period of time. Therefore, each participant is interviewed at two or more time points; each period of data collection is
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Stratification is sometimes introduced after the sampling phase in a process called "poststratification". This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying
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predictor of job performance is equally applicable across racial groups. Simple random sampling cannot accommodate the needs of researchers in this situation, because it does not provide subsamples of the population, and other sampling strategies, such as stratified sampling, can be used instead.
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Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at
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Theoretical sampling occurs when samples are selected on the basis of the results of the data collected so far with a goal of developing a deeper understanding of the area or develop theories. Extreme or very specific cases might be selected in order to maximize the likelihood a phenomenon will
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is a sample in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units
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that accuracy. (In the two examples of systematic sampling that are given above, much of the potential sampling error is due to variation between neighbouring houses â but because this method never selects two neighbouring houses, the sample will not give us any information on that variation.)
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For example, consider a street where the odd-numbered houses are all on the north (expensive) side of the road, and the even-numbered houses are all on the south (cheap) side. Under the sampling scheme given above, it is impossible to get a representative sample; either the houses sampled will
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Systematic sampling (also known as interval sampling) relies on arranging the study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every
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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
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classifier with
Gaussian distributions. The notion of minimax sampling is recently developed for a general class of classification rules, called class-wise smart classifiers. In this case, the sampling ratio of classes is selected so that the worst case classifier error over all the possible
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There are, however, some potential drawbacks to using stratified sampling. First, identifying strata and implementing such an approach can increase the cost and complexity of sample selection, as well as leading to increased complexity of population estimates. Second, when examining multiple
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Sometimes it is more cost-effective to select respondents in groups ('clusters'). Sampling is often clustered by geography, or by time periods. (Nearly all samples are in some sense 'clustered' in time â although this is rarely taken into account in the analysis.) For instance, if surveying
160:. More than two million people responded to the study with their names obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large, was deeply flawed.
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is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions.
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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, and randomly select one adult from each household. (For example, we can allocate each person a random number, generated from a
174:(ELD), their country's election commission, sample counts help reduce speculation and misinformation, while helping election officials to check against the election result for that electoral division. The reported sample counts yield a fairly accurate indicative result with a 95%
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Another option is probability proportional to size ('PPS') sampling, in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In a simple PPS design, these selection probabilities can then be used as the basis for
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Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited (or most cost-effective) for each identified subgroup within the population.
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produce five men and five women, but any given trial is likely to over represent one sex and underrepresent the other. Systematic and stratified techniques attempt to overcome this problem by "using information about the population" to choose a more "representative" sample.
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Choice-based sampling is one of the stratified sampling strategies. In choice-based sampling, the data are stratified on the target and a sample is taken from each stratum so that the rare target class will be more represented in the sample. The model is then built on this
707:. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample. The results usually must be adjusted to correct for the oversampling.
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In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage, and controller data are available at short time intervals. To predict down-time it may not be necessary to look at all the data but a sample may be sufficient.
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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 (preferably the entire population) with appropriate contact information. For example, in an
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In a simple random sample (SRS) of a given size, all subsets of a sampling frame have an equal probability of being selected. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given
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Simple random sampling can be vulnerable to sampling error because the randomness of the selection may result in a sample that does not reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will
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smaller chance of being interviewed. This can be accounted for using survey weights. Similarly, households with more than one telephone line have a greater chance of being selected in a random digit dialing sample, and weights can adjust for this.
583:'simple random sampling' because different subsets of the same size have different selection probabilities â e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection.
391:, placing limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population.
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Clustering can reduce travel and administrative costs. In the example above, an interviewer can make a single trip to visit several households in one block, rather than having to drive to a different block for each household.
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Dillman, D. A., Eltinge, J. L., Groves, R. M., & Little, R. J. A. (2002). "Survey nonresponse in design, data collection, and analysis". In: R. M. Groves, D. A. Dillman, J. L. Eltinge, & R. J. A. Little (Eds.),
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Singh, G N, Jaiswal, A. K., and Pandey A. K. (2021), Improved
Imputation Methods for Missing Data in Two-Occasion Successive Sampling, Communications in Statistics: Theory and Methods. DOI:10.1080/03610926.2021.1944211
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of its results over infinitely many trials), while his 'sample' was formed from observed results from that wheel. Similar considerations arise when taking repeated measurements of properties of materials such as the
825:. Then judgement is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
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have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
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program if it were made available nationwide. Here the superpopulation is "everybody in the country, given access to this treatment" â a group that does not yet exist since the program is not yet available to all.
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to sample. A cheaper method would be to use a stratified sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate.
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Time spent in making the sampled population and population of concern precise is often well spent because it raises many issues, ambiguities, and questions that would otherwise have been overlooked at this stage.
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However, systematic sampling is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be
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Are there controls within the research design or experiment which can serve to lessen the impact of a non-random convenience sample, thereby ensuring the results will be more representative of the population?
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Survey results are typically subject to some error. Total errors can be classified into sampling errors and non-sampling errors. The term "error" here includes systematic biases as well as random errors.
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Within any of the types of frames identified above, a variety of sampling methods can be employed individually or in combination. Factors commonly influencing the choice between these designs include:
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In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known. When every element in the population
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minimax ratio whose value is proved to be 0.5: in a binary classification, the class-sample sizes should be chosen equally. This ratio can be proved to be minimax ratio only under the assumption of
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be from the even-numbered, cheap side, unless the researcher has previous knowledge of this bias and avoids it by a using a skip which ensures jumping between the two sides (any odd-numbered skip).
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probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.
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involves finding a small group of initial respondents and using them to recruit more respondents. It is particularly useful in cases where the population is hidden or difficult to enumerate.
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After sampling, a review is held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis.
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First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.
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It is difficult to make generalizations from this sample because it may not represent the total population. Often, volunteers have a strong interest in the main topic of the survey.
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th element in the list. A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').
730:. However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections.
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efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population.
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Vehovar, V., Batagelj, Z., Manfreda, K.L., & Zaletel, M. (2002). "Nonresponse in web surveys". In: R. M. Groves, D. A. Dillman, J. L. Eltinge, & R. J. A. Little (Eds.),
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the day, nor is it necessary to look at all the tweets to determine the sentiment on each of the topics. A theoretical formulation for sampling
Twitter data has been developed.
517:=(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the
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Non-sampling errors are other errors which can impact final survey estimates, caused by problems in data collection, processing, or sample design. Such errors may include:
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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.
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home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.
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Is there good reason to believe that a particular convenience sample would or should respond or behave differently than a random sample from the same population?
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As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is
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Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors. These conditions give rise to
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the variable by which the list is ordered is correlated with the variable of interest. 'Every 10th' sampling is especially useful for efficient sampling from
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penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on periods or discrete occasions.
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is of high enough quality to be released to the customer or should be scrapped or reworked due to poor quality. In this case, the batch is the population.
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Dillman, D.A., Smyth, J.D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method. San
Francisco: Jossey-Bass.
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ASTM E122 Standard
Practice for Calculating Sample Size to Estimate, With a Specified Tolerable Error, the Average for Characteristic of a Lot or Process
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In imbalanced datasets, where the sampling ratio does not follow the population statistics, one can resample the dataset in a conservative manner called
224:, and used this to identify a biased wheel. In this case, the 'population' Jagger wanted to investigate was the overall behaviour of the wheel (i.e. the
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Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to
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but as the sample size that would be needed to achieve a particular upper bound on the sampling error with probability 1000/1001. His estimates used
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The historically important books by Deming and Kish remain valuable for insights for social scientists (particularly about the U.S. census and the
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Permits greater balancing of statistical power of tests of differences between strata by sampling equal numbers from strata varying widely in size.
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is a method of sampling elements in a region whereby an element is sampled if a chosen line segment, called a "transect", intersects the element.
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The textbook by Groves et alia provides an overview of survey methodology, including recent literature on questionnaire development (informed by
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between 0 and 1, and select the person with the highest number in each household). We then interview the selected person and find their income.
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are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population.
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It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-
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Lazarsfeld, P., & Fiske, M. (1938). The" panel" as a new tool for measuring opinion. The Public
Opinion Quarterly, 2(4), 596â612.
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households within a city, we might choose to select 100 city blocks and then interview every household within the selected blocks.
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sample than SRS to achieve the same level of accuracy â but cost savings from clustering might still make this a cheaper option.
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item non-response (submission or participation in survey but failing to complete one or more components/questions of the survey)
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associated with traditional cluster sampling. However, each sample may not be a full representative of the whole population.
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Sometimes what defines a population is obvious. For example, a manufacturer needs to decide whether a batch of material from
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2332:"Beyond the Existence Proof: Ontological Conditions, Epistemological Implications, and In-Depth Interview Research."],
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measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In
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Systematic sampling can also be adapted to a non-EPS approach; for an example, see discussion of PPS samples below.
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The variables upon which the population is stratified are strongly correlated with the desired dependent variable.
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Porter; Whitcomb; Weitzer (2004). "Multiple surveys of students and survey fatigue". In Porter, Stephen R (ed.).
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Successful statistical practice is based on focused problem definition. In sampling, this includes defining the "
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of survey sampling and require some knowledge of basic statistics, as discussed in the following textbooks:
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The voluntary sampling method is a type of non-probability sampling. Volunteers choose to complete a survey.
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Sampling of
Heterogeneous and Dynamic Material Systems: Theories of Heterogeneity, Sampling and Homogenizing
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Is the question being asked by the research one that can adequately be answered using a convenience sample?
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Random sampling by using lots is an old idea, mentioned several times in the Bible. In 1786, Pierre Simon
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within 4-5%; ELD reminded the public that sample counts are separate from official results, and only the
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Scott, A.J.; Wild, C.J. (1986). "Fitting logistic models under case-control or choice-based sampling".
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representative of the overall population, making the scheme less accurate than simple random sampling.
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sometimes called PPS-sequential or monetary unit sampling in the case of audits or forensic sampling.
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ASTM E141 Standard
Practice for Acceptance of Evidence Based on the Results of Probability Sampling
1792:
1396:
845:
225:
74:
and thus, it can provide insights in cases where it is infeasible to measure an entire population.
5218:
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1371:
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Measurement error: e.g. when respondents misunderstand a question, or find it difficult to answer
924:
374:
230:
70:
1130:
Formulas, tables, and power function charts are well known approaches to determine sample size.
5335:
5317:
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4778:
4708:
4501:
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2659:
2134:
Deepan
Palguna; Vikas Joshi; Venkatesan Chakaravarthy; Ravi Kothari; L. V. Subramaniam (2015).
1626:
454:
195:
163:
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2133:
1431:
1326:
Physical randomization devices such as coins, playing cards or sophisticated devices such as
1226:: Random variation in the results due to the elements in the sample being selected at random.
1220:: When the true selection probabilities differ from those assumed in calculating the results.
952:
variables such as spousal interaction. There have been several proposed methods of analyzing
2358:(1984). "Present Position and Potential Developments: Some Personal Views: Sample surveys".
2154:
Berinsky, A. J. (2008). "Survey non-response". In: W. Donsbach & M. W. Traugott (Eds.),
1538:
The elementary book by Scheaffer et alia uses quadratic equations from high-school algebra:
604:
A visual representation of selecting a random sample using the stratified sampling technique
504:
A visual representation of selecting a random sample using the systematic sampling technique
121:. He also computed probabilistic estimates of the error. These were not expressed as modern
5266:
5203:
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2941:
2821:
1459:
529:
403:
338:
2550:
ASTM E2234 Standard Practice for Sampling a Stream of Product by Attributes Indexed by AQL
1599:
8:
5426:
5395:
5148:
5056:
5031:
4873:
4798:
4721:
4402:
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2703:
2605:
2213:. New directions for institutional research. San Francisco: Jossey-Bass. pp. 63â74.
1550:
More mathematical statistics is required for Lohr, for SĂ€rndal et alia, and for Cochran:
1386:
1376:
1315:
904:
861:
822:
792:
758:
A visual representation of selecting a random sample using the cluster sampling technique
595:
533:
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354:
346:
342:
289:
175:
122:
98:
86:
5243:
5158:
5133:
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3391:
3353:
3324:
3284:
3244:
3190:
3107:
2793:
2788:
2499:
2411:
2392:(1993). "Populations and Selection: Limitations of Statistics (Presidential address)".
2377:
2008:
2004:
1926:
1757:
1646:
1486:
1347:
1257:
1236:
948:
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818:
411:
320:
selected from that household can be loosely viewed as also representing the person who
285:
94:
90:
54:
5360:
624:
at odds with the previously noted importance of utilizing criterion-relevant strata).
85:, weights can be applied to the data to adjust for the sample design, particularly in
5233:
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5128:
4882:
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4763:
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3933:
3921:
3547:
3464:
3408:
3331:
3175:
3097:
2876:
2750:
2547:
ASTM E1994 Standard Practice for Use of Process Oriented AOQL and LTPD Sampling Plans
2465:
2315:
2301:
2286:
2277:
2246:
2214:
1971:
1872:
1679:
1660:
1650:
1639:
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1585:
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1501:
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1366:
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1125:
976:
891:
610:
183:
130:
126:
50:
1966:
1949:
1306:
Weights can also serve other purposes, such as helping to correct for non-response.
158:
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2000:
1961:
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350:
202:
212:
In other cases, the examined 'population' may be even less tangible. For example,
5276:
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of the 100 selected blocks, rather than a household-level map of the whole city.
179:
146:
138:
118:
82:
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2329:
5286:
5168:
5163:
5118:
5113:
5051:
5026:
4684:
4679:
3142:
3072:
2718:
1493:
1436:
1223:
1217:
1199:
1102:('WOR' â no element can be selected more than once in the same sample) or
808:
777:
678:
Requires selection of relevant stratification variables which can be difficult.
407:
388:
276:
266:
2442:
2341:
636:
A stratified sampling approach is most effective when three conditions are met
5489:
5330:
5307:
5223:
5185:
5123:
4976:
4841:
4808:
4671:
4632:
4443:
4412:
3876:
3830:
3435:
3137:
2964:
2728:
2723:
2368:(The 150th Anniversary of the Royal Statistical Society, number 2): 208â221.
1733:
1598:
1426:
1361:
704:
525:
213:
102:
1834:"Presidential Election 2023: How Accurate Will The Sample Count Be Tonight?"
5281:
5083:
4783:
4716:
4693:
4608:
3938:
3234:
3132:
3067:
3009:
2994:
2931:
2886:
2454:
2450:
2424:
2389:
2355:
1975:
1248:
Under-coverage: sampling frame does not include elements in the population.
1213:
Sampling errors and biases are induced by the sample design. They include:
1152:
The intersection of the column and row is the minimum sample size required.
281:
241:
2453:(2001). "Biometrika centenary: Sample surveys". In D. M. Titterington and
1664:
4923:
4826:
4788:
4471:
4372:
4234:
4047:
4014:
3506:
3423:
3418:
3062:
3019:
2999:
2979:
2969:
2738:
2570:
2495:
2039:
1908:
1892:
1671:
1577:
662:
Allows use of different sampling techniques for different subpopulations.
459:
221:
101:
is used to determine if a production lot of material meets the governing
78:
37:
1062:
991:
853:
population statistics for class prior probabilities, would be the best.
357:. These various ways of probability sampling have two things in common:
5271:
3672:
3152:
2852:
2783:
2733:
2708:
2628:
2429:
2415:
2381:
2012:
1761:
1737:
953:
46:
2503:
600:
500:
3825:
3677:
3297:
3092:
3004:
2989:
2984:
2949:
1441:
541:
2407:
2373:
1753:
3341:
2959:
2836:
2831:
2826:
754:
217:
2059:"Voluntary Sampling Method combined with Social Media advertising"
382:
is any sampling method where some elements of the population have
361:
Every element has a known nonzero probability of being sampled and
186:
will declare the official results once vote counting is complete.
4846:
4547:
2535:
ASTM E105 Standard Practice for Probability Sampling Of Materials
841:
117:
estimated the population of France by using a sample, along with
114:
1924:
Scheaffer, Richard L.; William Mendenhal; R. Lyman Ott. (2006).
659:
Focuses on important subpopulations and ignores irrelevant ones.
613:. There are several potential benefits to stratified sampling.
5416:
5021:
4768:
3749:
3723:
3703:
2954:
2745:
2586:
1275:
unit nonresponse (lack of completion of any part of the survey)
1260:: failure to obtain complete data from all selected individuals
1245:
Over-coverage: inclusion of data from outside of the population
957:
829:
234:
473:
makes it relatively easy to estimate the accuracy of results.
433:
Availability of auxiliary information about units on the frame
2597:
1327:
1149:
Locate the column corresponding to the estimated effect size.
240:
This situation often arises when seeking knowledge about the
1742:
Journal of the Royal Statistical Society. Series A (General)
710:
170:, also known as the sample counts, whereas according to the
2688:
1542:
Scheaffer, Richard L., William Mendenhal and R. Lyman Ott.
463:
A visual representation of selecting a simple random sample
155:
1947:
5351:
Household, Income and Labour Dynamics in Australia Survey
2141:
International Joint Conference on Artificial Intelligence
1860:
1858:
1856:
1854:
939:
called a "wave". The method was developed by sociologist
1950:"Effect of separate sampling on classification accuracy"
1110:
each fish after catching it, this becomes a WOR design.
1948:
Shahrokh Esfahani, Mohammad; Dougherty, Edward (2014).
564:
be from the odd-numbered, expensive side, or they will
436:
Accuracy requirements, and the need to measure accuracy
2564:
1851:
1738:"A Sketch of the History of Survey Sampling in Russia"
1309:
681:
Is not useful when there are no homogeneous subgroups.
609:(or sample) to the size of the population is called a
2484:
Journal of the Operations Research Society of America
2245:(3rd ed.). New York, NY: John Wiley & Sons.
2206:
2166:
2164:
4510:
Autoregressive conditional heteroskedasticity (ARCH)
2544:
ASTM E1402 Standard Terminology Relating to Sampling
2158:(pp. 309â321). Thousand Oaks, CA: Sage Publications.
1702:
1511:
1337:
5417:
European Society for Opinion and Marketing Research
2040:SĂ€rndal, Carl-Erik; Swensson, Bengt; Wretman, Jan.
1943:
1941:
1939:
1909:SĂ€rndal, Carl-Erik; Swensson, Bengt; Wretman, Jan.
1554:
439:
Whether detailed analysis of the sample is expected
3972:
2395:Journal of the Royal Statistical Society, Series A
2361:Journal of the Royal Statistical Society, Series A
2272:Chambers, R L, and Skinner, C J (editors) (2003),
2161:
1992:Journal of the Royal Statistical Society, Series B
1925:
1638:
1146:Locate the row corresponding to the desired power
349:, probability-proportional-to-size sampling, and
5487:
5412:American Association for Public Opinion Research
5371:National Health and Nutrition Examination Survey
1936:
1864:
1787:
1143:Select the table corresponding to the selected α
1137:Postulate the effect size of interest, α, and ÎČ.
1093:
4058:Multivariate adaptive regression splines (MARS)
2197:(pp. 229â242). New York: John Wiley & Sons.
1809:"SAMPLE COUNT - Elections Department Singapore"
1709:. Web: MEASURE Evaluation. pp. 6â8, 62â64.
1158:
665:Improves the accuracy/efficiency of estimation.
41:A visual representation of the sampling process
5366:List of household surveys in the United States
2024:
2022:
1546:, Fifth Edition. Belmont: Duxbury Press, 1996.
5432:World Association for Public Opinion Research
4939:
2613:
2482:(May 1954). "Optimum preventative sampling".
2464:. Oxford University Press. pp. 165â194.
2289:(1975) On probability as a basis for action,
1500:" (5th edition). W.H. Freeman & Company.
1208:
300:according to their probability of selection.
5381:Suffolk University Political Research Center
2175:(pp. 3â26). New York: John Wiley & Sons.
2156:The Sage handbook of public opinion research
1113:
513:th element from then onwards. In this case,
414:. In addition, nonresponse effects may turn
2136:Analysis of Sampling Algorithms for Twitter
2030:
2019:
1891:
1719:Salant, Priscilla, I. Dillman, and A. Don.
1193:
1180:
1173:Noting comments and other contextual events
4953:
4946:
4932:
2658:
2620:
2606:
2056:
1776:Introduction to the Practice of Statistics
1498:Introduction to the practice of statistics
919:
368:
3271:
1988:
1965:
1271:. Two major types of non-response exist:
1254:Processing error: mistakes in data coding
844:. The minimax sampling has its origin in
817:, the population is first segmented into
717:Probability-proportional-to-size sampling
711:Probability-proportional-to-size sampling
446:
316:twice towards the total. (The person who
2420:(Portrait of T. M. F. Smith on page 144)
1602:; Swensson, Bengt; Wretman, Jan (1992).
1515:; Pisani, Robert; Purves, Roger (2007).
753:
646:Variability between strata are maximized
599:
499:
458:
402:Nonprobability sampling methods include
364:involves random selection at some point.
189:
133:and assumed that his sample was random.
69:for short) of individuals from within a
36:
5391:Quinnipiac University Polling Institute
2478:
2240:
1496:and George P. McCabe (February 2005). "
1042:
982:
776:It also means that one does not need a
643:Variability within strata are minimized
27:Selection of data points in statistics.
14:
5488:
5376:New Zealand Attitudes and Values Study
5323:Comparative Study of Electoral Systems
4584:KaplanâMeier estimator (product limit)
2351:, Hafner Publishing Company, New York
2310:Korn, E.L., and Graubard, B.I. (1999)
1865:Robert M. Groves; et al. (2009).
1774:David S. Moore and George P. McCabe. "
1732:
1633:
1167:Following the defined sampling process
856:
654:Advantages over other sampling methods
589:
489:
284:, possible sampling frames include an
150:prediction of a Republican win in the
4927:
4657:
4224:
3971:
3270:
3040:
2657:
2601:
2449:
2427:(2001). "Centenary: Sample surveys".
2423:
2388:
2354:
2091:
2089:
2057:Ariyaratne, Buddhika (30 July 2017).
1230:
1053:
898:
166:have adopted this practice since the
4894:
4594:Accelerated failure time (AFT) model
1576:
1133:Steps for using sample size tables:
1057:
986:
971:
722:stratification, as discussed above.
447:
5422:International Statistical Institute
4906:
4189:Analysis of variance (ANOVA, anova)
3041:
2565:U.S. federal and military standards
2210:Overcoming survey research problems
1310:Methods of producing random samples
835:
749:
528:, systematic sampling is a type of
421:
24:
5313:American National Election Studies
5303:List of comparative social surveys
4284:CochranâMantelâHaenszel statistics
2910:Pearson product-moment correlation
2349:Basic Ideas of Scientific Sampling
2263:
2241:Cochran, William G. (1977-01-01).
2101:
2086:
2050:
2005:10.1111/j.2517-6161.1986.tb01400.x
1831:
1258:Non-response or Participation bias
1037:active learning (machine learning)
1026:
532:. It is easy to implement and the
61:is the selection of a subset or a
25:
5517:
2579:
1791:; Pisani, Robert; Purves, Roger.
1293:
930:
802:
524:As long as the starting point is
260:
4905:
4893:
4881:
4868:
4867:
4658:
2585:
2243:Sampling Techniques, 3rd Edition
1832:Ho, Timothy (1 September 2023).
1340:
1061:
990:
4543:Least-squares spectral analysis
2234:
2200:
2187:
2178:
2148:
2127:
2118:
2077:
1982:
1917:
1902:
1703:Lance, P.; Hattori, A. (2016).
1322:pseudo-random number generators
1163:Good data collection involves:
943:in 1938 as a means of studying
536:induced can make it efficient,
430:Nature and quality of the frame
337:Probability sampling includes:
154:went badly awry, due to severe
3524:Mean-unbiased minimum-variance
2627:
2109:"Examples of sampling methods"
2042:Model Assisted Survey Sampling
1911:Model Assisted Survey Sampling
1885:
1825:
1801:
1781:
1768:
1726:
1721:How to conduct your own survey
1713:
1696:
1604:Model assisted survey sampling
1267:A particular problem involves
1170:Keeping the data in time order
684:Can be expensive to implement.
13:
1:
4837:Geographic information system
4053:Simultaneous equations models
2033:Sampling: Design and Analysis
1967:10.1093/bioinformatics/btt662
1896:Sampling: Design and analysis
1689:
1623:Institute for Social Research
1582:Sampling: Design and analysis
1485:The other books focus on the
1412:Pseudo-random number sampling
1094:Replacement of selected units
137:introduced sample surveys to
4020:Coefficient of determination
3631:Uniformly most powerful test
2554:
2511:
1320:Mathematical algorithms for
1159:Sampling and data collection
966:structural equation modeling
890:In social science research,
7:
5356:International Social Survey
4589:Proportional hazards models
4533:Spectral density estimation
4515:Vector autoregression (VAR)
3949:Maximum posterior estimator
3181:Randomized controlled trial
2330:10.1007%2Fs11135-012-9775-3
1333:
791:is commonly implemented as
135:Alexander Ivanovich Chuprov
10:
5522:
4349:Multivariate distributions
2769:Average absolute deviation
2312:Analysis of Health Surveys
2293:, 29(4), pp. 146â152.
1928:Elementary survey sampling
1544:Elementary survey sampling
1519:(4th ed.). New York:
1382:HorvitzâThompson estimator
1234:
1209:Sampling errors and biases
1197:
1123:
1117:
1046:
902:
806:
761:
714:
593:
493:
452:
372:
264:
108:
29:
5440:
5404:
5386:The Phillips Academy Poll
5295:
5259:
5214:Exploratory data analysis
5184:
5104:
5067:Sample size determination
5012:
4962:
4863:
4817:
4754:
4707:
4670:
4666:
4653:
4625:
4607:
4574:
4565:
4523:
4470:
4431:
4380:
4371:
4337:Structural equation model
4292:
4249:
4245:
4220:
4179:
4145:
4099:
4066:
4028:
3995:
3991:
3967:
3907:
3816:
3735:
3699:
3690:
3673:Score/Lagrange multiplier
3658:
3611:
3556:
3482:
3473:
3283:
3279:
3266:
3225:
3199:
3151:
3106:
3088:Sample size determination
3053:
3049:
3036:
2940:
2895:
2869:
2851:
2807:
2759:
2679:
2670:
2666:
2653:
2635:
2342:10.1007/s11135-012-9775-3
2324:Lucas, Samuel R. (2012).
2291:The American Statistician
1562:(Third ed.). Wiley.
1417:Sample size determination
1402:Random-sampling mechanism
1120:Sample size determination
1114:Sample size determination
442:Cost/operational concerns
216:studied the behaviour of
32:Sampling (disambiguation)
4832:Environmental statistics
4354:Elliptical distributions
4147:Generalized linear model
4076:Simple linear regression
3846:HodgesâLehmann estimator
3303:Probability distribution
3212:Stochastic approximation
2774:Coefficient of variation
1453:
1397:Replication (statistics)
1194:Errors in sample surveys
1181:Applications of sampling
1140:Check sample size table
1098:Sampling schemes may be
1023:actually be observable.
226:probability distribution
5327:Emerson College Polling
5219:Multivariate statistics
5062:Nonprobability sampling
4492:Cross-correlation (XCF)
4100:Non-standard predictors
3534:LehmannâScheffĂ© theorem
3207:Adaptive clinical trial
2529:
2443:10.1093/biomet/88.1.167
2274:Analysis of Survey Data
1723:. No. 300.723 S3. 1994.
1706:Sampling and Evaluation
1641:Some Theory of Sampling
1422:Sampling (case studies)
1407:Resampling (statistics)
1176:Recording non-responses
925:Line-intercept sampling
920:Line-intercept sampling
821:sub-groups, just as in
448:Simple random sampling
380:Nonprobability sampling
375:Nonprobability sampling
369:Nonprobability sampling
275:As a remedy, we seek a
231:electrical conductivity
5336:European Social Survey
5318:Asian Barometer Survey
5209:Descriptive statistics
5094:Cross-sequential study
5047:Simple random sampling
4888:Mathematics portal
4709:Engineering statistics
4617:NelsonâAalen estimator
4194:Analysis of covariance
4081:Ordinary least squares
4005:Pearson product-moment
3409:Statistical functional
3320:Empirical distribution
3153:Controlled experiments
2882:Frequency distribution
2660:Descriptive statistics
2516:
2334:Quality & Quantity
2063:heal-info.blogspot.com
1627:University of Michigan
759:
743:
605:
505:
464:
455:Simple random sampling
400:
339:simple random sampling
327:
220:wheels at a casino in
164:Elections in Singapore
71:statistical population
42:
5496:Sampling (statistics)
5346:General Social Survey
5229:Statistical inference
5089:Cross-sectional study
4804:Population statistics
4746:System identification
4480:Autocorrelation (ACF)
4408:Exponential smoothing
4322:Discriminant analysis
4317:Canonical correlation
4181:Partition of variance
4043:Regression validation
3887:(JonckheereâTerpstra)
3786:Likelihood-ratio test
3475:Frequentist inference
3387:Locationâscale family
3308:Sampling distribution
3273:Statistical inference
3240:Cross-sectional study
3227:Observational studies
3186:Randomized experiment
3015:Stem-and-leaf display
2817:Central limit theorem
2592:Sampling (statistics)
1432:Sampling distribution
1224:Random sampling error
968:with lagged effects.
903:Further information:
757:
736:
603:
503:
462:
393:
302:
190:Population definition
152:presidential election
40:
5267:Audience measurement
5204:Level of measurement
5037:Sampling for surveys
4727:Probabilistic design
4312:Principal components
4155:Exponential families
4107:Nonlinear regression
4086:General linear model
4048:Mixed effects models
4038:Errors and residuals
4015:Confounding variable
3917:Bayesian probability
3895:Van der Waerden test
3885:Ordered alternative
3650:Multiple comparisons
3529:RaoâBlackwellization
3492:Estimating equations
3448:Statistical distance
3166:Factorial experiment
2699:Arithmetic-Geometric
2594:at Wikimedia Commons
2496:10.1287/opre.2.2.197
2347:Stuart, Alan (1962)
2300:, Elsevier Science,
2065:. Health Informatics
1460:cognitive psychology
1372:Gy's sampling theory
1043:Judgmental selection
983:Theoretical sampling
874:opportunity sampling
864:(sometimes known as
530:probability sampling
404:convenience sampling
308:uniform distribution
172:Elections Department
144:In the US, the 1936
123:confidence intervals
30:For other uses, see
5427:Pew Research Center
5396:World Values Survey
5139:Specification error
5057:Stratified sampling
4799:Official statistics
4722:Methods engineering
4403:Seasonal adjustment
4171:Poisson regressions
4091:Bayesian regression
4030:Regression analysis
4010:Partial correlation
3982:Regression analysis
3581:Prediction interval
3576:Likelihood interval
3566:Confidence interval
3558:Interval estimation
3519:Unbiased estimators
3337:Model specification
3217:Up-and-down designs
2905:Partial correlation
2861:Index of dispersion
2779:Interquartile range
2462:: One Hundred Years
1606:. Springer-Verlag.
1560:Sampling techniques
1556:Cochran, William G.
1387:Official statistics
1377:German tank problem
1316:Random number table
1100:without replacement
945:political campaigns
905:Self-selection bias
862:Accidental sampling
857:Accidental sampling
823:stratified sampling
793:multistage sampling
596:Stratified sampling
590:Stratified sampling
496:Systematic sampling
490:Systematic sampling
355:multistage sampling
347:stratified sampling
343:systematic sampling
290:telephone directory
176:confidence interval
99:Acceptance sampling
87:stratified sampling
5501:Survey methodology
5234:Statistical models
5134:Non-sampling error
5032:Statistical sample
4972:Collection methods
4819:Spatial statistics
4699:Medical statistics
4599:First hitting time
4553:Whittle likelihood
4204:Degrees of freedom
4199:Multivariate ANOVA
4132:Heteroscedasticity
3944:Bayesian estimator
3909:Bayesian inference
3758:KolmogorovâSmirnov
3643:Randomization test
3613:Testing hypotheses
3586:Tolerance interval
3497:Maximum likelihood
3392:Exponential family
3325:Density estimation
3285:Statistical theory
3245:Natural experiment
3191:Scientific control
3108:Survey methodology
2794:Standard deviation
2287:Deming, W. Edwards
2195:Survey nonresponse
2173:Survey nonresponse
2097:Survey Methodology
1868:Survey methodology
1838:DollarsAndSense.sg
1647:Dover Publications
1635:Deming, W. Edwards
1600:SĂ€rndal, Carl-Erik
1487:statistical theory
1471:Survey methodology
1348:Mathematics portal
1237:Non-sampling error
1231:Non-sampling error
1073:. You can help by
1054:Haphazard sampling
1002:. You can help by
899:Voluntary sampling
819:mutually exclusive
760:
689:Poststratification
606:
506:
465:
412:purposive sampling
297:probability sample
286:electoral register
95:statistical theory
91:probability theory
63:statistical sample
55:survey methodology
43:
5506:Scientific method
5483:
5482:
5199:Contingency table
5174:Processing errors
5159:Non-response bias
5149:Measurement error
5129:Systematic errors
4921:
4920:
4859:
4858:
4855:
4854:
4794:National accounts
4764:Actuarial science
4756:Social statistics
4649:
4648:
4645:
4644:
4641:
4640:
4576:Survival function
4561:
4560:
4423:Granger causality
4264:Contingency table
4239:Survival analysis
4216:
4215:
4212:
4211:
4068:Linear regression
3963:
3962:
3959:
3958:
3934:Credible interval
3903:
3902:
3686:
3685:
3502:Method of moments
3371:Parametric family
3332:Statistical model
3262:
3261:
3258:
3257:
3176:Random assignment
3098:Statistical power
3032:
3031:
3028:
3027:
2877:Contingency table
2847:
2846:
2714:Generalized/power
2590:Media related to
2471:978-0-19-850993-6
2252:978-0-471-16240-7
2095:Groves, et alia.
1656:978-0-486-64684-8
1613:978-0-387-40620-6
1591:978-0-534-35361-2
1569:978-0-471-16240-7
1530:978-0-393-92972-0
1367:Estimation theory
1126:Sample complexity
1091:
1090:
1020:
1019:
977:Snowball sampling
972:Snowball sampling
892:snowball sampling
611:sampling fraction
184:returning officer
131:prior probability
51:quality assurance
16:(Redirected from
5513:
5194:Categorical data
4948:
4941:
4934:
4925:
4924:
4909:
4908:
4897:
4896:
4886:
4885:
4871:
4870:
4774:Crime statistics
4668:
4667:
4655:
4654:
4572:
4571:
4538:Fourier analysis
4525:Frequency domain
4505:
4452:
4418:Structural break
4378:
4377:
4327:Cluster analysis
4274:Log-linear model
4247:
4246:
4222:
4221:
4163:
4137:Homoscedasticity
3993:
3992:
3969:
3968:
3888:
3880:
3872:
3871:(KruskalâWallis)
3856:
3841:
3796:Cross validation
3781:
3763:AndersonâDarling
3710:
3697:
3696:
3668:Likelihood-ratio
3660:Parametric tests
3638:Permutation test
3621:1- & 2-tails
3512:Minimum distance
3484:Point estimation
3480:
3479:
3431:Optimal decision
3382:
3281:
3280:
3268:
3267:
3250:Quasi-experiment
3200:Adaptive designs
3051:
3050:
3038:
3037:
2915:Rank correlation
2677:
2676:
2668:
2667:
2655:
2654:
2622:
2615:
2608:
2599:
2598:
2589:
2507:
2475:
2446:
2419:
2385:
2257:
2256:
2238:
2232:
2231:
2229:
2227:
2204:
2198:
2191:
2185:
2182:
2176:
2168:
2159:
2152:
2146:
2145:
2131:
2125:
2122:
2116:
2115:
2113:
2105:
2099:
2093:
2084:
2081:
2075:
2074:
2072:
2070:
2054:
2048:
2045:
2036:
2031:Lohr, Sharon L.
2026:
2017:
2016:
1986:
1980:
1979:
1969:
1945:
1934:
1933:
1931:
1921:
1915:
1914:
1906:
1900:
1899:
1889:
1883:
1882:
1862:
1849:
1848:
1846:
1844:
1829:
1823:
1822:
1820:
1818:
1813:
1805:
1799:
1798:
1785:
1779:
1772:
1766:
1765:
1730:
1724:
1717:
1711:
1710:
1700:
1668:
1644:
1617:
1595:
1573:
1534:
1473:(2010 2nd ed. )
1350:
1345:
1344:
1288:opportunity cost
1104:with replacement
1086:
1083:
1065:
1058:
1015:
1012:
994:
987:
842:minimax sampling
836:Minimax sampling
789:Cluster sampling
764:Cluster sampling
750:Cluster sampling
728:Poisson sampling
449:
422:Sampling methods
21:
5521:
5520:
5516:
5515:
5514:
5512:
5511:
5510:
5486:
5485:
5484:
5479:
5436:
5400:
5361:LatinobarĂłmetro
5291:
5277:Market research
5255:
5180:
5154:Response errors
5100:
5074:Research design
5042:Random sampling
5008:
4992:Semi-structured
4964:Data collection
4958:
4956:survey research
4952:
4922:
4917:
4880:
4851:
4813:
4750:
4736:quality control
4703:
4685:Clinical trials
4662:
4637:
4621:
4609:Hazard function
4603:
4557:
4519:
4503:
4466:
4462:BreuschâGodfrey
4450:
4427:
4367:
4342:Factor analysis
4288:
4269:Graphical model
4241:
4208:
4175:
4161:
4141:
4095:
4062:
4024:
3987:
3986:
3955:
3899:
3886:
3878:
3870:
3854:
3839:
3818:Rank statistics
3812:
3791:Model selection
3779:
3737:Goodness of fit
3731:
3708:
3682:
3654:
3607:
3552:
3541:Median unbiased
3469:
3380:
3313:Order statistic
3275:
3254:
3221:
3195:
3147:
3102:
3045:
3043:Data collection
3024:
2936:
2891:
2865:
2843:
2803:
2755:
2672:Continuous data
2662:
2649:
2631:
2626:
2582:
2567:
2557:
2532:
2525:ISO 3951 series
2522:ISO 2859 series
2519:
2514:
2472:
2451:Smith, T. M. F.
2425:Smith, T. M. F.
2408:10.2307/2982726
2390:Smith, T. M. F.
2374:10.2307/2981677
2356:Smith, T. M. F.
2266:
2264:Further reading
2261:
2260:
2253:
2239:
2235:
2225:
2223:
2221:
2205:
2201:
2192:
2188:
2183:
2179:
2169:
2162:
2153:
2149:
2132:
2128:
2123:
2119:
2111:
2107:
2106:
2102:
2094:
2087:
2082:
2078:
2068:
2066:
2055:
2051:
2027:
2020:
1987:
1983:
1946:
1937:
1922:
1918:
1907:
1903:
1893:Lohr, Sharon L.
1890:
1886:
1879:
1863:
1852:
1842:
1840:
1830:
1826:
1816:
1814:
1811:
1807:
1806:
1802:
1789:Freedman, David
1786:
1782:
1773:
1769:
1754:10.2307/2981944
1731:
1727:
1718:
1714:
1701:
1697:
1692:
1676:Survey Sampling
1657:
1614:
1592:
1578:Lohr, Sharon L.
1570:
1531:
1513:Freedman, David
1456:
1451:
1447:Survey sampling
1392:Ratio estimator
1357:Data collection
1346:
1339:
1336:
1312:
1296:
1284:survey sampling
1239:
1233:
1211:
1202:
1196:
1183:
1161:
1128:
1122:
1116:
1108:tag and release
1096:
1087:
1081:
1078:
1071:needs expansion
1056:
1051:
1049:Judgment sample
1045:
1033:active sampling
1029:
1027:Active sampling
1016:
1010:
1007:
1000:needs expansion
985:
974:
941:Paul Lazarsfeld
933:
922:
907:
901:
859:
838:
811:
805:
766:
752:
719:
713:
598:
592:
498:
492:
457:
451:
424:
377:
371:
269:
263:
192:
180:margin of error
147:Literary Digest
139:Imperial Russia
129:with a uniform
119:ratio estimator
111:
89:. Results from
83:survey sampling
35:
28:
23:
22:
18:Random sampling
15:
12:
11:
5:
5519:
5509:
5508:
5503:
5498:
5481:
5480:
5478:
5477:
5476:
5475:
5470:
5465:
5460:
5455:
5447:
5441:
5438:
5437:
5435:
5434:
5429:
5424:
5419:
5414:
5408:
5406:
5402:
5401:
5399:
5398:
5393:
5388:
5383:
5378:
5373:
5368:
5363:
5358:
5353:
5348:
5343:
5338:
5333:
5328:
5325:
5320:
5315:
5310:
5305:
5299:
5297:
5293:
5292:
5290:
5289:
5287:Public opinion
5284:
5279:
5274:
5269:
5263:
5261:
5257:
5256:
5254:
5253:
5252:
5251:
5246:
5241:
5231:
5226:
5221:
5216:
5211:
5206:
5201:
5196:
5190:
5188:
5182:
5181:
5179:
5178:
5177:
5176:
5171:
5169:Pseudo-opinion
5166:
5164:Coverage error
5161:
5156:
5151:
5146:
5141:
5131:
5126:
5121:
5119:Standard error
5116:
5114:Sampling error
5110:
5108:
5102:
5101:
5099:
5098:
5097:
5096:
5091:
5086:
5081:
5071:
5070:
5069:
5064:
5059:
5054:
5052:Quota sampling
5049:
5044:
5034:
5029:
5027:Sampling frame
5024:
5018:
5016:
5010:
5009:
5007:
5006:
5005:
5004:
4999:
4994:
4989:
4979:
4974:
4968:
4966:
4960:
4959:
4951:
4950:
4943:
4936:
4928:
4919:
4918:
4916:
4915:
4903:
4891:
4877:
4864:
4861:
4860:
4857:
4856:
4853:
4852:
4850:
4849:
4844:
4839:
4834:
4829:
4823:
4821:
4815:
4814:
4812:
4811:
4806:
4801:
4796:
4791:
4786:
4781:
4776:
4771:
4766:
4760:
4758:
4752:
4751:
4749:
4748:
4743:
4738:
4729:
4724:
4719:
4713:
4711:
4705:
4704:
4702:
4701:
4696:
4691:
4682:
4680:Bioinformatics
4676:
4674:
4664:
4663:
4651:
4650:
4647:
4646:
4643:
4642:
4639:
4638:
4636:
4635:
4629:
4627:
4623:
4622:
4620:
4619:
4613:
4611:
4605:
4604:
4602:
4601:
4596:
4591:
4586:
4580:
4578:
4569:
4563:
4562:
4559:
4558:
4556:
4555:
4550:
4545:
4540:
4535:
4529:
4527:
4521:
4520:
4518:
4517:
4512:
4507:
4499:
4494:
4489:
4488:
4487:
4485:partial (PACF)
4476:
4474:
4468:
4467:
4465:
4464:
4459:
4454:
4446:
4441:
4435:
4433:
4432:Specific tests
4429:
4428:
4426:
4425:
4420:
4415:
4410:
4405:
4400:
4395:
4390:
4384:
4382:
4375:
4369:
4368:
4366:
4365:
4364:
4363:
4362:
4361:
4346:
4345:
4344:
4334:
4332:Classification
4329:
4324:
4319:
4314:
4309:
4304:
4298:
4296:
4290:
4289:
4287:
4286:
4281:
4279:McNemar's test
4276:
4271:
4266:
4261:
4255:
4253:
4243:
4242:
4218:
4217:
4214:
4213:
4210:
4209:
4207:
4206:
4201:
4196:
4191:
4185:
4183:
4177:
4176:
4174:
4173:
4157:
4151:
4149:
4143:
4142:
4140:
4139:
4134:
4129:
4124:
4119:
4117:Semiparametric
4114:
4109:
4103:
4101:
4097:
4096:
4094:
4093:
4088:
4083:
4078:
4072:
4070:
4064:
4063:
4061:
4060:
4055:
4050:
4045:
4040:
4034:
4032:
4026:
4025:
4023:
4022:
4017:
4012:
4007:
4001:
3999:
3989:
3988:
3985:
3984:
3979:
3973:
3965:
3964:
3961:
3960:
3957:
3956:
3954:
3953:
3952:
3951:
3941:
3936:
3931:
3930:
3929:
3924:
3913:
3911:
3905:
3904:
3901:
3900:
3898:
3897:
3892:
3891:
3890:
3882:
3874:
3858:
3855:(MannâWhitney)
3850:
3849:
3848:
3835:
3834:
3833:
3822:
3820:
3814:
3813:
3811:
3810:
3809:
3808:
3803:
3798:
3788:
3783:
3780:(ShapiroâWilk)
3775:
3770:
3765:
3760:
3755:
3747:
3741:
3739:
3733:
3732:
3730:
3729:
3721:
3712:
3700:
3694:
3692:Specific tests
3688:
3687:
3684:
3683:
3681:
3680:
3675:
3670:
3664:
3662:
3656:
3655:
3653:
3652:
3647:
3646:
3645:
3635:
3634:
3633:
3623:
3617:
3615:
3609:
3608:
3606:
3605:
3604:
3603:
3598:
3588:
3583:
3578:
3573:
3568:
3562:
3560:
3554:
3553:
3551:
3550:
3545:
3544:
3543:
3538:
3537:
3536:
3531:
3516:
3515:
3514:
3509:
3504:
3499:
3488:
3486:
3477:
3471:
3470:
3468:
3467:
3462:
3457:
3456:
3455:
3445:
3440:
3439:
3438:
3428:
3427:
3426:
3421:
3416:
3406:
3401:
3396:
3395:
3394:
3389:
3384:
3368:
3367:
3366:
3361:
3356:
3346:
3345:
3344:
3339:
3329:
3328:
3327:
3317:
3316:
3315:
3305:
3300:
3295:
3289:
3287:
3277:
3276:
3264:
3263:
3260:
3259:
3256:
3255:
3253:
3252:
3247:
3242:
3237:
3231:
3229:
3223:
3222:
3220:
3219:
3214:
3209:
3203:
3201:
3197:
3196:
3194:
3193:
3188:
3183:
3178:
3173:
3168:
3163:
3157:
3155:
3149:
3148:
3146:
3145:
3143:Standard error
3140:
3135:
3130:
3129:
3128:
3123:
3112:
3110:
3104:
3103:
3101:
3100:
3095:
3090:
3085:
3080:
3075:
3073:Optimal design
3070:
3065:
3059:
3057:
3047:
3046:
3034:
3033:
3030:
3029:
3026:
3025:
3023:
3022:
3017:
3012:
3007:
3002:
2997:
2992:
2987:
2982:
2977:
2972:
2967:
2962:
2957:
2952:
2946:
2944:
2938:
2937:
2935:
2934:
2929:
2928:
2927:
2922:
2912:
2907:
2901:
2899:
2893:
2892:
2890:
2889:
2884:
2879:
2873:
2871:
2870:Summary tables
2867:
2866:
2864:
2863:
2857:
2855:
2849:
2848:
2845:
2844:
2842:
2841:
2840:
2839:
2834:
2829:
2819:
2813:
2811:
2805:
2804:
2802:
2801:
2796:
2791:
2786:
2781:
2776:
2771:
2765:
2763:
2757:
2756:
2754:
2753:
2748:
2743:
2742:
2741:
2736:
2731:
2726:
2721:
2716:
2711:
2706:
2704:Contraharmonic
2701:
2696:
2685:
2683:
2674:
2664:
2663:
2651:
2650:
2648:
2647:
2642:
2636:
2633:
2632:
2625:
2624:
2617:
2610:
2602:
2596:
2595:
2581:
2580:External links
2578:
2577:
2576:
2573:
2566:
2563:
2562:
2561:
2556:
2553:
2552:
2551:
2548:
2545:
2542:
2539:
2536:
2531:
2528:
2527:
2526:
2523:
2518:
2515:
2513:
2510:
2509:
2508:
2490:(2): 197â203.
2476:
2470:
2447:
2437:(1): 167â243.
2421:
2402:(2): 144â166.
2386:
2352:
2345:
2322:
2308:
2306:978-0444556066
2294:
2284:
2270:
2265:
2262:
2259:
2258:
2251:
2233:
2219:
2199:
2186:
2177:
2160:
2147:
2126:
2117:
2100:
2085:
2076:
2049:
2047:
2046:
2037:
2018:
1999:(2): 170â182.
1981:
1960:(2): 242â250.
1954:Bioinformatics
1935:
1916:
1901:
1884:
1878:978-0470465462
1877:
1850:
1824:
1800:
1780:
1767:
1748:(2): 118â125.
1725:
1712:
1694:
1693:
1691:
1688:
1687:
1686:
1669:
1655:
1619:
1618:
1612:
1596:
1590:
1574:
1568:
1548:
1547:
1536:
1535:
1529:
1509:
1494:David S. Moore
1483:
1482:
1455:
1452:
1450:
1449:
1444:
1439:
1437:Sampling error
1434:
1429:
1424:
1419:
1414:
1409:
1404:
1399:
1394:
1389:
1384:
1379:
1374:
1369:
1364:
1359:
1353:
1352:
1351:
1335:
1332:
1331:
1330:
1324:
1318:
1311:
1308:
1295:
1294:Survey weights
1292:
1280:
1279:
1276:
1262:
1261:
1255:
1252:
1249:
1246:
1235:Main article:
1232:
1229:
1228:
1227:
1221:
1218:Selection bias
1210:
1207:
1200:Sampling error
1198:Main article:
1195:
1192:
1182:
1179:
1178:
1177:
1174:
1171:
1168:
1160:
1157:
1156:
1155:
1154:
1153:
1150:
1147:
1144:
1138:
1118:Main article:
1115:
1112:
1095:
1092:
1089:
1088:
1068:
1066:
1055:
1052:
1047:Main article:
1044:
1041:
1028:
1025:
1018:
1017:
997:
995:
984:
981:
973:
970:
936:Panel sampling
932:
931:Panel sampling
929:
921:
918:
900:
897:
888:
887:
884:
881:
858:
855:
837:
834:
815:quota sampling
809:Quota sampling
807:Main article:
804:
803:Quota sampling
801:
778:sampling frame
762:Main article:
751:
748:
715:Main article:
712:
709:
700:
699:
691:
690:
686:
685:
682:
679:
675:
674:
670:
669:
666:
663:
660:
656:
655:
651:
650:
647:
644:
640:
639:
637:
594:Main article:
591:
588:
534:stratification
494:Main article:
491:
488:
453:Main article:
450:
445:
444:
443:
440:
437:
434:
431:
423:
420:
408:quota sampling
389:exclusion bias
373:Main article:
370:
367:
366:
365:
362:
277:sampling frame
267:Sampling frame
265:Main article:
262:
261:Sampling frame
259:
191:
188:
141:in the 1870s.
127:Bayes' theorem
110:
107:
103:specifications
26:
9:
6:
4:
3:
2:
5518:
5507:
5504:
5502:
5499:
5497:
5494:
5493:
5491:
5474:
5471:
5469:
5466:
5464:
5461:
5459:
5456:
5454:
5451:
5450:
5448:
5446:
5443:
5442:
5439:
5433:
5430:
5428:
5425:
5423:
5420:
5418:
5415:
5413:
5410:
5409:
5407:
5403:
5397:
5394:
5392:
5389:
5387:
5384:
5382:
5379:
5377:
5374:
5372:
5369:
5367:
5364:
5362:
5359:
5357:
5354:
5352:
5349:
5347:
5344:
5342:
5339:
5337:
5334:
5332:
5331:Eurobarometer
5329:
5326:
5324:
5321:
5319:
5316:
5314:
5311:
5309:
5308:Afrobarometer
5306:
5304:
5301:
5300:
5298:
5296:Major surveys
5294:
5288:
5285:
5283:
5280:
5278:
5275:
5273:
5270:
5268:
5265:
5264:
5262:
5258:
5250:
5247:
5245:
5242:
5240:
5237:
5236:
5235:
5232:
5230:
5227:
5225:
5224:Psychometrics
5222:
5220:
5217:
5215:
5212:
5210:
5207:
5205:
5202:
5200:
5197:
5195:
5192:
5191:
5189:
5187:
5186:Data analysis
5183:
5175:
5172:
5170:
5167:
5165:
5162:
5160:
5157:
5155:
5152:
5150:
5147:
5145:
5142:
5140:
5137:
5136:
5135:
5132:
5130:
5127:
5125:
5124:Sampling bias
5122:
5120:
5117:
5115:
5112:
5111:
5109:
5107:
5106:Survey errors
5103:
5095:
5092:
5090:
5087:
5085:
5082:
5080:
5077:
5076:
5075:
5072:
5068:
5065:
5063:
5060:
5058:
5055:
5053:
5050:
5048:
5045:
5043:
5040:
5039:
5038:
5035:
5033:
5030:
5028:
5025:
5023:
5020:
5019:
5017:
5015:
5011:
5003:
5000:
4998:
4995:
4993:
4990:
4988:
4985:
4984:
4983:
4980:
4978:
4977:Questionnaire
4975:
4973:
4970:
4969:
4967:
4965:
4961:
4957:
4949:
4944:
4942:
4937:
4935:
4930:
4929:
4926:
4914:
4913:
4904:
4902:
4901:
4892:
4890:
4889:
4884:
4878:
4876:
4875:
4866:
4865:
4862:
4848:
4845:
4843:
4842:Geostatistics
4840:
4838:
4835:
4833:
4830:
4828:
4825:
4824:
4822:
4820:
4816:
4810:
4809:Psychometrics
4807:
4805:
4802:
4800:
4797:
4795:
4792:
4790:
4787:
4785:
4782:
4780:
4777:
4775:
4772:
4770:
4767:
4765:
4762:
4761:
4759:
4757:
4753:
4747:
4744:
4742:
4739:
4737:
4733:
4730:
4728:
4725:
4723:
4720:
4718:
4715:
4714:
4712:
4710:
4706:
4700:
4697:
4695:
4692:
4690:
4686:
4683:
4681:
4678:
4677:
4675:
4673:
4672:Biostatistics
4669:
4665:
4661:
4656:
4652:
4634:
4633:Log-rank test
4631:
4630:
4628:
4624:
4618:
4615:
4614:
4612:
4610:
4606:
4600:
4597:
4595:
4592:
4590:
4587:
4585:
4582:
4581:
4579:
4577:
4573:
4570:
4568:
4564:
4554:
4551:
4549:
4546:
4544:
4541:
4539:
4536:
4534:
4531:
4530:
4528:
4526:
4522:
4516:
4513:
4511:
4508:
4506:
4504:(BoxâJenkins)
4500:
4498:
4495:
4493:
4490:
4486:
4483:
4482:
4481:
4478:
4477:
4475:
4473:
4469:
4463:
4460:
4458:
4457:DurbinâWatson
4455:
4453:
4447:
4445:
4442:
4440:
4439:DickeyâFuller
4437:
4436:
4434:
4430:
4424:
4421:
4419:
4416:
4414:
4413:Cointegration
4411:
4409:
4406:
4404:
4401:
4399:
4396:
4394:
4391:
4389:
4388:Decomposition
4386:
4385:
4383:
4379:
4376:
4374:
4370:
4360:
4357:
4356:
4355:
4352:
4351:
4350:
4347:
4343:
4340:
4339:
4338:
4335:
4333:
4330:
4328:
4325:
4323:
4320:
4318:
4315:
4313:
4310:
4308:
4305:
4303:
4300:
4299:
4297:
4295:
4291:
4285:
4282:
4280:
4277:
4275:
4272:
4270:
4267:
4265:
4262:
4260:
4259:Cohen's kappa
4257:
4256:
4254:
4252:
4248:
4244:
4240:
4236:
4232:
4228:
4223:
4219:
4205:
4202:
4200:
4197:
4195:
4192:
4190:
4187:
4186:
4184:
4182:
4178:
4172:
4168:
4164:
4158:
4156:
4153:
4152:
4150:
4148:
4144:
4138:
4135:
4133:
4130:
4128:
4125:
4123:
4120:
4118:
4115:
4113:
4112:Nonparametric
4110:
4108:
4105:
4104:
4102:
4098:
4092:
4089:
4087:
4084:
4082:
4079:
4077:
4074:
4073:
4071:
4069:
4065:
4059:
4056:
4054:
4051:
4049:
4046:
4044:
4041:
4039:
4036:
4035:
4033:
4031:
4027:
4021:
4018:
4016:
4013:
4011:
4008:
4006:
4003:
4002:
4000:
3998:
3994:
3990:
3983:
3980:
3978:
3975:
3974:
3970:
3966:
3950:
3947:
3946:
3945:
3942:
3940:
3937:
3935:
3932:
3928:
3925:
3923:
3920:
3919:
3918:
3915:
3914:
3912:
3910:
3906:
3896:
3893:
3889:
3883:
3881:
3875:
3873:
3867:
3866:
3865:
3862:
3861:Nonparametric
3859:
3857:
3851:
3847:
3844:
3843:
3842:
3836:
3832:
3831:Sample median
3829:
3828:
3827:
3824:
3823:
3821:
3819:
3815:
3807:
3804:
3802:
3799:
3797:
3794:
3793:
3792:
3789:
3787:
3784:
3782:
3776:
3774:
3771:
3769:
3766:
3764:
3761:
3759:
3756:
3754:
3752:
3748:
3746:
3743:
3742:
3740:
3738:
3734:
3728:
3726:
3722:
3720:
3718:
3713:
3711:
3706:
3702:
3701:
3698:
3695:
3693:
3689:
3679:
3676:
3674:
3671:
3669:
3666:
3665:
3663:
3661:
3657:
3651:
3648:
3644:
3641:
3640:
3639:
3636:
3632:
3629:
3628:
3627:
3624:
3622:
3619:
3618:
3616:
3614:
3610:
3602:
3599:
3597:
3594:
3593:
3592:
3589:
3587:
3584:
3582:
3579:
3577:
3574:
3572:
3569:
3567:
3564:
3563:
3561:
3559:
3555:
3549:
3546:
3542:
3539:
3535:
3532:
3530:
3527:
3526:
3525:
3522:
3521:
3520:
3517:
3513:
3510:
3508:
3505:
3503:
3500:
3498:
3495:
3494:
3493:
3490:
3489:
3487:
3485:
3481:
3478:
3476:
3472:
3466:
3463:
3461:
3458:
3454:
3451:
3450:
3449:
3446:
3444:
3441:
3437:
3436:loss function
3434:
3433:
3432:
3429:
3425:
3422:
3420:
3417:
3415:
3412:
3411:
3410:
3407:
3405:
3402:
3400:
3397:
3393:
3390:
3388:
3385:
3383:
3377:
3374:
3373:
3372:
3369:
3365:
3362:
3360:
3357:
3355:
3352:
3351:
3350:
3347:
3343:
3340:
3338:
3335:
3334:
3333:
3330:
3326:
3323:
3322:
3321:
3318:
3314:
3311:
3310:
3309:
3306:
3304:
3301:
3299:
3296:
3294:
3291:
3290:
3288:
3286:
3282:
3278:
3274:
3269:
3265:
3251:
3248:
3246:
3243:
3241:
3238:
3236:
3233:
3232:
3230:
3228:
3224:
3218:
3215:
3213:
3210:
3208:
3205:
3204:
3202:
3198:
3192:
3189:
3187:
3184:
3182:
3179:
3177:
3174:
3172:
3169:
3167:
3164:
3162:
3159:
3158:
3156:
3154:
3150:
3144:
3141:
3139:
3138:Questionnaire
3136:
3134:
3131:
3127:
3124:
3122:
3119:
3118:
3117:
3114:
3113:
3111:
3109:
3105:
3099:
3096:
3094:
3091:
3089:
3086:
3084:
3081:
3079:
3076:
3074:
3071:
3069:
3066:
3064:
3061:
3060:
3058:
3056:
3052:
3048:
3044:
3039:
3035:
3021:
3018:
3016:
3013:
3011:
3008:
3006:
3003:
3001:
2998:
2996:
2993:
2991:
2988:
2986:
2983:
2981:
2978:
2976:
2973:
2971:
2968:
2966:
2965:Control chart
2963:
2961:
2958:
2956:
2953:
2951:
2948:
2947:
2945:
2943:
2939:
2933:
2930:
2926:
2923:
2921:
2918:
2917:
2916:
2913:
2911:
2908:
2906:
2903:
2902:
2900:
2898:
2894:
2888:
2885:
2883:
2880:
2878:
2875:
2874:
2872:
2868:
2862:
2859:
2858:
2856:
2854:
2850:
2838:
2835:
2833:
2830:
2828:
2825:
2824:
2823:
2820:
2818:
2815:
2814:
2812:
2810:
2806:
2800:
2797:
2795:
2792:
2790:
2787:
2785:
2782:
2780:
2777:
2775:
2772:
2770:
2767:
2766:
2764:
2762:
2758:
2752:
2749:
2747:
2744:
2740:
2737:
2735:
2732:
2730:
2727:
2725:
2722:
2720:
2717:
2715:
2712:
2710:
2707:
2705:
2702:
2700:
2697:
2695:
2692:
2691:
2690:
2687:
2686:
2684:
2682:
2678:
2675:
2673:
2669:
2665:
2661:
2656:
2652:
2646:
2643:
2641:
2638:
2637:
2634:
2630:
2623:
2618:
2616:
2611:
2609:
2604:
2603:
2600:
2593:
2588:
2584:
2583:
2574:
2572:
2569:
2568:
2560:ANSI/ASQ Z1.4
2559:
2558:
2549:
2546:
2543:
2540:
2537:
2534:
2533:
2524:
2521:
2520:
2505:
2501:
2497:
2493:
2489:
2485:
2481:
2477:
2473:
2467:
2463:
2459:
2456:
2452:
2448:
2444:
2440:
2436:
2432:
2431:
2426:
2422:
2417:
2413:
2409:
2405:
2401:
2397:
2396:
2391:
2387:
2383:
2379:
2375:
2371:
2367:
2363:
2362:
2357:
2353:
2350:
2346:
2343:
2339:
2335:
2331:
2327:
2323:
2321:
2320:0-471-13773-1
2317:
2313:
2309:
2307:
2303:
2299:
2296:Gy, P (2012)
2295:
2292:
2288:
2285:
2283:
2282:0-471-89987-9
2279:
2275:
2271:
2268:
2267:
2254:
2248:
2244:
2237:
2222:
2220:9780787974770
2216:
2212:
2211:
2203:
2196:
2190:
2181:
2174:
2167:
2165:
2157:
2151:
2143:
2142:
2137:
2130:
2121:
2110:
2104:
2098:
2092:
2090:
2080:
2064:
2060:
2053:
2043:
2038:
2034:
2029:
2028:
2025:
2023:
2014:
2010:
2006:
2002:
1998:
1994:
1993:
1985:
1977:
1973:
1968:
1963:
1959:
1955:
1951:
1944:
1942:
1940:
1930:
1929:
1920:
1912:
1905:
1897:
1894:
1888:
1880:
1874:
1870:
1867:
1861:
1859:
1857:
1855:
1839:
1835:
1828:
1810:
1804:
1796:
1795:
1790:
1784:
1777:
1771:
1763:
1759:
1755:
1751:
1747:
1743:
1739:
1735:
1729:
1722:
1716:
1708:
1707:
1699:
1695:
1685:
1684:0-471-10949-5
1681:
1677:
1673:
1670:
1666:
1662:
1658:
1652:
1648:
1643:
1642:
1636:
1632:
1631:
1630:
1628:
1624:
1615:
1609:
1605:
1601:
1597:
1593:
1587:
1583:
1579:
1575:
1571:
1565:
1561:
1557:
1553:
1552:
1551:
1545:
1541:
1540:
1539:
1532:
1526:
1522:
1518:
1514:
1510:
1507:
1506:0-7167-6282-X
1503:
1499:
1495:
1492:
1491:
1490:
1488:
1480:
1479:0-471-48348-6
1476:
1472:
1468:
1467:Robert Groves
1465:
1464:
1463:
1461:
1448:
1445:
1443:
1440:
1438:
1435:
1433:
1430:
1428:
1427:Sampling bias
1425:
1423:
1420:
1418:
1415:
1413:
1410:
1408:
1405:
1403:
1400:
1398:
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1368:
1365:
1363:
1362:Design effect
1360:
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1085:
1076:
1072:
1069:This section
1067:
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1038:
1034:
1024:
1014:
1005:
1001:
998:This section
996:
993:
989:
988:
980:
978:
969:
967:
963:
962:growth curves
959:
955:
950:
946:
942:
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774:
770:
765:
756:
747:
742:
741:
735:
731:
729:
723:
718:
708:
706:
705:biased sample
697:
696:
695:
688:
687:
683:
680:
677:
676:
673:Disadvantages
672:
671:
667:
664:
661:
658:
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648:
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642:
641:
638:
635:
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244:of which the
243:
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219:
215:
214:Joseph Jagger
210:
206:
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187:
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177:
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169:
168:2015 election
165:
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128:
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106:
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68:
64:
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56:
52:
48:
39:
33:
19:
5405:Associations
5282:Opinion poll
5260:Applications
5084:Cohort study
5041:
4997:Unstructured
4910:
4898:
4879:
4872:
4784:Econometrics
4734: /
4717:Chemometrics
4694:Epidemiology
4687: /
4660:Applications
4502:ARIMA model
4449:Q-statistic
4398:Stationarity
4294:Multivariate
4237: /
4233: /
4231:Multivariate
4229: /
4169: /
4165: /
3939:Bayes factor
3838:Signed rank
3750:
3724:
3716:
3704:
3399:Completeness
3235:Cohort study
3133:Opinion poll
3115:
3068:Missing data
3055:Study design
3010:Scatter plot
2932:Scatter plot
2925:Spearman's Ï
2887:Grouped data
2575:MIL-STD-1916
2487:
2483:
2461:
2458:
2434:
2428:
2399:
2393:
2365:
2359:
2348:
2333:
2311:
2297:
2290:
2273:
2242:
2236:
2224:. Retrieved
2209:
2202:
2194:
2189:
2180:
2172:
2155:
2150:
2139:
2135:
2129:
2120:
2103:
2096:
2079:
2067:. Retrieved
2062:
2052:
2041:
2032:
1996:
1990:
1984:
1957:
1953:
1927:
1919:
1910:
1904:
1895:
1887:
1869:
1866:
1841:. Retrieved
1837:
1827:
1815:. Retrieved
1803:
1793:
1783:
1775:
1770:
1745:
1741:
1728:
1720:
1715:
1705:
1698:
1675:
1672:Kish, Leslie
1640:
1620:
1603:
1581:
1559:
1549:
1543:
1537:
1516:
1497:
1484:
1470:
1457:
1305:
1301:
1297:
1281:
1269:non-response
1268:
1266:
1263:
1240:
1212:
1203:
1188:
1184:
1162:
1132:
1129:
1103:
1099:
1097:
1079:
1075:adding to it
1070:
1030:
1021:
1008:
1004:adding to it
999:
975:
956:, including
949:longitudinal
935:
934:
923:
915:
911:
908:
889:
873:
869:
865:
860:
839:
827:
814:
812:
797:
787:
783:
775:
771:
767:
744:
738:
737:
732:
724:
720:
701:
698:Oversampling
692:
630:
626:
622:
618:
615:
607:
585:
580:
578:
572:
570:
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561:
558:
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401:
395:
394:
383:
379:
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336:
330:
328:
323:
319:
314:
313:
304:
303:
296:
294:
282:opinion poll
274:
270:
255:
251:
245:
242:cause system
239:
211:
207:
200:
193:
162:
145:
143:
112:
76:
66:
62:
58:
44:
5341:Gallup Poll
5144:Frame error
5079:Panel study
5014:Methodology
4912:WikiProject
4827:Cartography
4789:Jurimetrics
4741:Reliability
4472:Time domain
4451:(LjungâBox)
4373:Time-series
4251:Categorical
4235:Time-series
4227:Categorical
4162:(Bernoulli)
3997:Correlation
3977:Correlation
3773:JarqueâBera
3745:Chi-squared
3507:M-estimator
3460:Asymptotics
3404:Sufficiency
3171:Interaction
3083:Replication
3063:Effect size
3020:Violin plot
3000:Radar chart
2980:Forest plot
2970:Correlogram
2920:Kendall's Ï
2571:MIL-STD-105
2480:Whittle, P.
2124:Cohen, 1988
2069:18 December
1843:3 September
1817:3 September
1584:. Duxbury.
1469:, et alia.
870:convenience
222:Monte Carlo
79:observation
5490:Categories
5473:Statistics
5463:Psychology
5272:Demography
5249:Structural
5244:Log-linear
4987:Structured
4779:Demography
4497:ARMA model
4302:Regression
3879:(Friedman)
3840:(Wilcoxon)
3778:Normality
3768:Lilliefors
3715:Student's
3591:Resampling
3465:Robustness
3453:divergence
3443:Efficiency
3381:(monotone)
3376:Likelihood
3293:Population
3126:Stratified
3078:Population
2897:Dependence
2853:Count data
2784:Percentile
2761:Dispersion
2694:Arithmetic
2629:Statistics
2460:Biometrika
2430:Biometrika
1794:Statistics
1734:Seneta, E.
1690:References
1517:Statistics
1124:See also:
954:panel data
526:randomized
479:on average
324:selected.)
203:production
196:population
47:statistics
5468:Sociology
5449:Projects
5239:Graphical
4982:Interview
4160:Logistic
3927:posterior
3853:Rank sum
3601:Jackknife
3596:Bootstrap
3414:Bootstrap
3349:Parameter
3298:Statistic
3093:Statistic
3005:Run chart
2990:Pie chart
2985:Histogram
2975:Fan chart
2950:Bar chart
2832:L-moments
2719:Geometric
2555:ANSI, ASQ
2512:Standards
2455:D. R. Cox
2314:, Wiley,
2276:, Wiley,
1678:, Wiley,
1462:) :
1442:Sortition
1082:July 2024
1011:July 2015
542:databases
5458:Politics
5453:Business
5445:Category
4874:Category
4567:Survival
4444:Johansen
4167:Binomial
4122:Isotonic
3709:(normal)
3354:location
3161:Blocking
3116:Sampling
2995:QâQ plot
2960:Box plot
2942:Graphics
2837:Skewness
2827:Kurtosis
2799:Variance
2729:Heronian
2724:Harmonic
1976:24257187
1736:(1985).
1637:(1966).
1580:(1999).
1558:(1977).
1334:See also
846:Anderson
573:quantify
246:observed
218:roulette
65:(termed
59:sampling
4954:Social
4900:Commons
4847:Kriging
4732:Process
4689:studies
4548:Wavelet
4381:General
3548:Plug-in
3342:L space
3121:Cluster
2822:Moments
2640:Outline
2457:(ed.).
2416:2982726
2382:2981677
2226:15 July
2013:2345712
1762:2981944
1674:(1995)
1625:at the
947:. This
351:cluster
115:Laplace
109:History
5022:Census
5002:Couple
4769:Census
4359:Normal
4307:Manova
4127:Robust
3877:2-way
3869:1-way
3707:-test
3378:
2955:Biplot
2746:Median
2739:Lehmer
2681:Center
2504:166605
2502:
2468:
2414:
2380:
2318:
2304:
2280:
2249:
2217:
2011:
1974:
1875:
1760:
1682:
1665:166526
1663:
1653:
1610:
1588:
1566:
1527:
1521:Norton
1504:
1477:
964:, and
958:MANOVA
830:random
410:, and
288:and a
235:copper
67:sample
53:, and
4393:Trend
3922:prior
3864:anova
3753:-test
3727:-test
3719:-test
3626:Power
3571:Pivot
3364:shape
3359:scale
2809:Shape
2789:Range
2734:Heinz
2709:Cubic
2645:Index
2500:JSTOR
2412:JSTOR
2378:JSTOR
2112:(PDF)
2009:JSTOR
1812:(PDF)
1758:JSTOR
1454:Notes
1328:ERNIE
322:isn't
178:at a
77:Each
4626:Test
3826:Sign
3678:Wald
2751:Mode
2689:Mean
2530:ASTM
2466:ISBN
2316:ISBN
2302:ISBN
2278:ISBN
2247:ISBN
2228:2019
2215:ISBN
2071:2018
1972:PMID
1873:ISBN
1845:2023
1819:2023
1680:ISBN
1661:OCLC
1651:ISBN
1608:ISBN
1586:ISBN
1564:ISBN
1525:ISBN
1502:ISBN
1475:ISBN
866:grab
470:pair
331:does
156:bias
93:and
3806:BIC
3801:AIC
2517:ISO
2492:doi
2439:doi
2404:doi
2400:156
2370:doi
2366:147
2338:doi
2326:doi
2001:doi
1962:doi
1750:doi
1746:148
1629:):
1282:In
1077:.
1031:In
1006:.
872:or
850:LDA
813:In
581:not
566:all
562:all
416:any
353:or
233:of
45:In
5492::
2498:.
2486:.
2435:88
2433:.
2410:.
2398:.
2376:.
2364:.
2336:,
2163:^
2138:.
2088:^
2061:.
2021:^
2007:.
1997:48
1995:.
1970:.
1958:30
1956:.
1952:.
1938:^
1871:.
1853:^
1836:.
1778:".
1756:.
1744:.
1740:.
1659:.
1649:.
1645:.
1523:.
1039:.
960:,
868:,
554:un
544:.
538:if
406:,
384:no
345:,
341:,
318:is
295:A
292:.
237:.
105:.
57:,
49:,
4947:e
4940:t
4933:v
3751:G
3725:F
3717:t
3705:Z
3424:V
3419:U
2621:e
2614:t
2607:v
2506:.
2494::
2488:2
2474:.
2445:.
2441::
2418:.
2406::
2384:.
2372::
2344:.
2340::
2328::
2255:.
2230:.
2144:.
2114:.
2073:.
2044:.
2035:.
2015:.
2003::
1978:.
1964::
1932:.
1913:.
1898:.
1881:.
1847:.
1821:.
1797:.
1764:.
1752::
1667:.
1616:.
1594:.
1572:.
1533:.
1508:.
1481:.
1084:)
1080:(
1013:)
1009:(
519:k
515:k
511:k
34:.
20:)
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