표집

수학노트
둘러보기로 가기 검색하러 가기

노트

위키데이터

말뭉치

  1. The probability sampling method is based on the likelihood that each member of a population has an equal chance of being selected to be in the sample.[1]
  2. Most researchers agree that this form of sampling is the closest to representing the actual population, as human bias is eliminated with the use of computational randomization.[1]
  3. One of the key advantages of probability sampling is that it is the easiest to measure for error.[1]
  4. This type of sampling guarantees that each member of a population has an equal chance of being included in the sample.[1]
  5. The reporter asked a sampling of people about their eating habits.[2]
  6. a sampling of the menu's entrées We were given a sampling of the food.[2]
  7. With the interest-rate-setting Federal Open Market Committee preparing to meet Dec. 15-16, here is a sampling of what Fed officials have said since their last gathering in November.[2]
  8. Just a sampling of the virus's deadly reach is breathtaking.[2]
  9. If rangeland vegetation was homogeneous, designing a sampling regime would be fairly straightforward.[3]
  10. Designing effective sampling regimes to accommodate this inherent variability is the real challenge of rangeland inventory or monitoring programs.[3]
  11. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population.[4]
  12. There are several different sampling techniques available that can be grouped into two categories as probability sampling, and non-probability sampling.[4]
  13. In probability sampling, alternatively knows as random sampling, you start with a complete sample frame of all eligible individuals that have an equal chance to be part of the selected sample.[4]
  14. It is typically assumed that statistical tests contain data that has been obtained through random sampling.[4]
  15. Field sampling programs provide the information needed to determine the status and dynamics of populations and communities and thus are the basis for many kind of research.[5]
  16. Site selection is an important decision in any sampling program and it should be based to the study objectives.[5]
  17. Because most sampling program involves comparisons among sites, they should be physically similar in order to meaningfully compared and to avoid any confound conclusion.[5]
  18. Choice of biological units in sampling programs.[5]
  19. The book begins with an introduction to standard probability sampling concepts, which provides the foundation for studying samples selected from a finite population.[6]
  20. Sampling is frequently used because gathering data on every member of a target population or every product produced by a company is often impossible, impractical, or too costly to collect.[7]
  21. The primary line of defense against sampling bias is good judgment, based on prior experience dealing with the population being studied.[8]
  22. From a narrow perspective, if we limit ourselves to one particular way of collecting data, we face a clear trade-off: Large samples limit our exposure to sampling error, but are very costly.[8]
  23. In practice, such sampling is almost always done without replacement.[8]
  24. In such a case, data is frequently collected using systematic sampling.[8]
  25. Stratified sampling techniques are generally used when the population is heterogeneous, or dissimilar, where certain homogeneous, or similar, sub-populations can be isolated (strata).[9]
  26. Simple random sampling is most appropriate when the entire population from which the sample is taken is homogeneous.[9]
  27. Non-probability Sampling: Why don't we use non-probability sampling schemes?[10]
  28. Multi-Stage Sampling: Sometimes the population is too large and scattered for it to be practical to make a list of the entire population from which to draw a SRS.[10]
  29. The formulas in almost all statistics books assume simple random sampling.[10]
  30. Unless you are willing to learn the more complex techniques to analyze the data after it is collected, it is appropriate to use simple random sampling.[10]
  31. In sampling, we assume that samples are drawn from the population and sample means and population means are equal.[11]
  32. Simple random sampling: By using the random number generator technique, the researcher draws a sample from the population called simple random sampling.[11]
  33. In this type of sampling method, a researcher starts from a random point and selects every nth subject in the sampling frame.[11]
  34. Stratified simple random sampling: In stratified simple random sampling, a proportion from strata of the population is selected using simple random sampling.[11]
  35. See for more information the specific sampling procedure card Retail packages and finished articles Take an appropriate number of cans, bottles, bags or jars as a sample.[12]
  36. Markov chain sampling methods originated with the work of Metropolis, Rosenbluth, Rosenbluth, Teller and Teller (1953) who proposed an algorithm to simulate a high dimensional discrete distribution.[13]
  37. Under independent sampling from the posterior, which is rarely feasible, this calculation would be justified by classical laws of large numbers.[13]
  38. A sample is collected from a sampling frame, or the set of information about the accessible units in a sample.[14]
  39. Probability sampling involves random selection, each person in the group or community has an equal chance of being chosen.[14]
  40. Non-probability sampling does not involve random selection and so cannot rely on probability theory to ensure that it is representative of the population of interest.[14]
  41. However, many researchers use nonprobability sampling because in many cases, probability sampling is not practical, feasible, or ethical.[14]
  42. Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population.[15]
  43. The sampling process comprises of several stage.[15]
  44. The second step in the sampling process is to choose a sampling frame .[15]
  45. Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalizable to the population.[15]
  46. Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen.[16]
  47. Random sampling is one of the simplest forms of collecting data from the total population.[16]
  48. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process.[16]
  49. Hence, some variations when drawing results can come up, which is known as a sampling error.[16]
  50. Cluster sampling works best when the clusters are similar in character to each other.[17]
  51. To understand what sampling error is, you first need to know a little bit about sampling and what it means in survey research.[18]
  52. To make sure that your sample is a fair representation, you need to follow some survey sampling best practices.[18]
  53. But there’s more to doing sampling well than just getting the right sample size.[18]
  54. Somewhat confusingly, the term ‘sampling error’ doesn’t mean mistakes researchers have made when selecting or working with a sample.[18]
  55. Sampling, in statistics, a process or method of drawing a representative group of individuals or cases from a particular population.[19]
  56. The basic sampling design is simple random sampling, based on probability theory.[19]
  57. In this form of random sampling, every element of the population being sampled has an equal probability of being selected.[19]
  58. Sampling based on probability theory allows the investigator to determine the likelihood that statistical findings are the result of chance.[19]
  59. Another reason for sampling is that not all units in the population can be identified, such as all the air molecules in the LA basin.[20]
  60. However, you must be aware of problems that can arise in systematic random sampling.[20]
  61. Stratified random sampling: Each unit in the population is identified, and each unit has a known, non-zero chance of being in the sample.[20]
  62. Instead, you may divide students into the five groups and then select the same number of students from each group using a simple random sampling method.[20]
  63. Pew Research Center also conducts international surveys that involve sampling and interviewing people in multiple countries.[21]
  64. Some special challenges arise when sampling these populations.[21]
  65. In particular, it may be difficult to find a sampling frame or list for the population of interest and this may influence how the population is defined.[21]
  66. Non-probability sampling uses non-random techniques (i.e. the judgment of the researcher).[22]
  67. In systematic sampling , you select sample elements from an ordered frame.[22]
  68. A sampling frame is just a list of participants that you want to get a sample from.[22]
  69. In stratified sampling, sample each subpopulation independently.[22]
  70. The representation of this two is performed either by the method of probability random sampling or by the method of non-probability random sampling.[23]
  71. The selection of random type is done by probability random sampling while the non-selection type is by non-probability probability random sampling.[23]
  72. The aim of this article is to discuss about the sampling and sampling technicality.[23]
  73. Statistical agencies prefer the probability random sampling.[23]
  74. Sampling helps a lot in research.[24]
  75. The process of selecting a sample is known as sampling.[24]
  76. This Sampling technique uses randomization to make sure that every element of the population gets an equal chance to be part of the selected sample.[24]
  77. All the elements of the cluster are used for sampling.[24]
  78. The early part of the chapter outlines the probabilistic sampling methods.[25]
  79. These include simple random sampling, systematic sampling, stratified sampling and cluster sampling.[25]
  80. Thereafter, the principal non-probability method, quota sampling, is explained and its strengths and weaknesses outlined.[25]
  81. The statistical aspects of sampling are then explored.[25]
  82. Then, because some types of sampling rely upon quantitative models, we’ll talk about some of the statistical terms used in sampling .[26]
  83. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual.[27]
  84. There are several different sampling techniques available, and they can be subdivided into two groups: probability sampling and non-probability sampling.[27]
  85. In probability (random) sampling, you start with a complete sampling frame of all eligible individuals from which you select your sample.[27]
  86. Probability sampling methods tend to be more time-consuming and expensive than non-probability sampling.[27]
  87. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights.[28]
  88. Sampling in market research is of two types – probability sampling and non-probability sampling.[28]
  89. Probability sampling is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly.[28]
  90. In non-probability sampling, the researcher chooses members for research at random.[28]
  91. Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population.[29]
  92. Key Takeaways Certified Public Accountants use sampling during audits to determine the accuracy and completeness of account balances.[29]
  93. A Certified Public Accountant (CPA) performing a financial audit uses sampling to determine the accuracy and completeness of account balances in the financial statements.[29]
  94. Sampling performed by an auditor is referred to as "audit sampling.[29]
  95. A commonly seen unit of sampling rate is Hz, which stands for Hertz and means "samples per second".[30]
  96. Sampling rates higher than about 50 kHz to 60 kHz cannot supply more usable information for human listeners.[30]
  97. Even though ultrasonic frequencies are inaudible to humans, recording and mixing at higher sampling rates is effective in eliminating the distortion that can be caused by foldback aliasing.[30]
  98. For most phonemes, almost all of the energy is contained in the 100 Hz–4 kHz range, allowing a sampling rate of 8 kHz.[30]
  99. One of the problems that can occur when selecting a sample from a target population is sampling bias.[31]
  100. The sampling frame is the actual list of individuals that the sample will be drawn from.[32]
  101. Probability sampling means that every member of the population has a chance of being selected.[32]
  102. Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct.[32]
  103. Stratified sampling involves dividing the population into subpopulations that may differ in important ways.[32]
  104. In sampling, this includes defining the "population" from which our sample is drawn.[33]
  105. In such cases, sampling theory may treat the observed population as a sample from a larger 'superpopulation'.[33]
  106. Probability sampling includes: Simple Random Sampling, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling.[33]
  107. Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors.[33]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Sampling Methods
  2. ↑ 2.0 2.1 2.2 2.3 Definition of Sampling by Merriam-Webster
  3. ↑ 3.0 3.1 Rangeland Inventory, Monitoring, and Evaluation: Sampling Concepts
  4. ↑ 4.0 4.1 4.2 4.3 What is Random Sampling?
  5. ↑ 5.0 5.1 5.2 5.3 Coastal Wiki
  6. ↑ Sampling Statistics
  7. ↑ Sampling in Quality Control - What is Quality Sampling?
  8. ↑ 8.0 8.1 8.2 8.3 Sampling
  9. ↑ 9.0 9.1 Sampling
  10. ↑ 10.0 10.1 10.2 10.3 Types of Sampling
  11. ↑ 11.0 11.1 11.2 11.3 Statistics Solutions
  12. ↑ Sampling procedures
  13. ↑ 13.0 13.1 Sampling Method - an overview
  14. ↑ 14.0 14.1 14.2 14.3 Sampling
  15. ↑ 15.0 15.1 15.2 15.3 Research Methods for the Social Sciences
  16. ↑ 16.0 16.1 16.2 16.3 What is Random Sampling? Definition of Random Sampling, Random Sampling Meaning
  17. ↑ Sampling
  18. ↑ 18.0 18.1 18.2 18.3 Sampling Errors: Definition & 5 Most Common Types
  19. ↑ 19.0 19.1 19.2 19.3 Sampling | statistics
  20. ↑ 20.0 20.1 20.2 20.3 Sampling
  21. ↑ 21.0 21.1 21.2 Sampling - Pew Research Center Methods
  22. ↑ 22.0 22.1 22.2 22.3 Sampling in Statistics: Different Sampling Methods, Types & Error
  23. ↑ 23.0 23.1 23.2 23.3 Sampling and sampling methods
  24. ↑ 24.0 24.1 24.2 24.3 Sampling Techniques
  25. ↑ 25.0 25.1 25.2 25.3 Chapter 7: Sampling In Marketing Research
  26. ↑ Sampling
  27. ↑ 27.0 27.1 27.2 27.3 Methods of sampling from a population
  28. ↑ 28.0 28.1 28.2 28.3 Types of Sampling: Sampling Methods with Examples
  29. ↑ 29.0 29.1 29.2 29.3 Sampling Definition
  30. ↑ 30.0 30.1 30.2 30.3 Sampling (signal processing)
  31. ↑ Simply Psychology
  32. ↑ 32.0 32.1 32.2 32.3 Types and Techniques Explained
  33. ↑ 33.0 33.1 33.2 33.3 Sampling (statistics)

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LEMMA': 'sampling'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=표집&oldid=51222"