the definition of random sampling需要英文解释

the definition of random sampling需要英文解释
the definition of random sampling
需要英文解释

the definition of random sampling需要英文解释
random sampling - the selection of a random sample; each element of the population has an equal chance of been selected
或者
A selection of elements by a formal randomizing device for purposes of inference about a population of inference from that population in such a way that the probability of each possible outcome may be precisely specified in advance; the inferences are necessarily stochastic.（这个貌似更标准些：）

Random sampling - all members of the population have an equal chance of being selected as part of the sample. You might think this means just standing in the street and asking passers-by to answer you...

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Random sampling - all members of the population have an equal chance of being selected as part of the sample. You might think this means just standing in the street and asking passers-by to answer your questions. However, there would be many members of the population who would not be in the street at the time you are there, therefore, they do not stand any chance of being part of your sample. To pick a random sample, it is necessary to take all the names on the electoral register( a list of all the people who live in a particualr area) and pick out, for example, every fiftieth name. This particular person needs to be interviewed to make the sample truly random. Random sampling is very expensive and time consuming, but gives a true sample of the population.

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随机抽样的定义
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the pur...

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随机抽样的定义
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice.
sampling is divided in two categories 1. Probability Sampling 2. Nonprobability Sampling
Probability sampling includes: Simple Random Method, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling.
Nonprobability Sampling includes: Accidental Sampling, Quota Sampling and Purposive Sampling.
Simple random sampling
In a simple random sample ('SRS') of a given size, all such subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. This minimises bias and simplifies analysis of results. However, it can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population.
For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to overrepresent one sex and underrepresent the other. Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample.

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