Definition of statistical inference in English:

statistical inference

noun

mass noun

The theory, methods, and practice of forming judgements about the parameters of a population and the reliability of statistical relationships, typically on the basis of random sampling.
the problem is fundamental to statistical inference
count noun the available data can be used to make statistical inferences
Example sentencesExamples
- Without recombination, data would reflect a single realization of this process, making statistical inference a questionable project.
- Using the new family of models, we investigate the utility of a variety of new statistical inference procedures.
- In the jargon of statistical inference this means that the relationship between deflation and depression is not ‘statistically significant.’
- For the system at hand, likelihood methods may provide a more reliable basis for statistical inference.
- The F-ratio associated with the sheep/goat variable should have been 1.57, not 1.33 as reported, although this should not have affected statistical inference.
- However, for anyone trained in statistical inference and experimental methodology, this will appear as just another blatant attempt to snatch victory out of the jaws of defeat.
- His articles are more of a contribution to probability theory than to simultaneous statistical inference, and the reader in search of a convenient reference for such use might prefer.
- However, shifting one's means of justification implies a corresponding shift to an alternative model of statistical inference, and in some cases requires moving away from statistically based inference altogether.
- The development of theoretical models that can aid in understanding complicated demographic histories and provide a basis for methods of statistical inference has been another major aim of recent work.
- Other articles written by Savage relate to statistical inference, in particular the Bayesian approach.
- To that end, statistical inference using Poisson random field models provides powerful likelihood and Bayesian methods for quantifying some of these forces, such as mutation and directional selection.
- The purpose of this paper has been to acquaint cognitive science researchers with some of the core differences between the randomization and normal curve models of statistical inference.
- This method falls within a novel approach to robust statistical inference from incomplete databases based on probability intervals.
- Not only must these models be more realistic, they must also permit fast computation in frequentist and Bayesian statistical inference.
- Qualitative sampling can be confusing, especially if one's knowledge regarding sampling methods originates from a framework of statistical inference.
- In statistical inference we infer certain facts about a large population - say, the average height of New Yorkers - from measurements made on a sample.
- Second, the sample size is small; hence, the power of statistical inference is potentially limited in this study.
- We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics.
- In medical research the periodic calls for a wholesale switch to the use of bayesian statistical inference have been largely ignored.
- Because these data are not drawn from a random sample, generalizations are based on intuitive plausibility rather than statistical inference.

Definition of statistical inference in US English:

statistical inference

noun

The theory, methods, and practice of forming judgments about the parameters of a population and the reliability of statistical relationships, typically on the basis of random sampling.
the problem is fundamental to statistical inference
count noun the available data can be used to make statistical inferences
Example sentencesExamples
- The F-ratio associated with the sheep/goat variable should have been 1.57, not 1.33 as reported, although this should not have affected statistical inference.
- However, for anyone trained in statistical inference and experimental methodology, this will appear as just another blatant attempt to snatch victory out of the jaws of defeat.
- Other articles written by Savage relate to statistical inference, in particular the Bayesian approach.
- Not only must these models be more realistic, they must also permit fast computation in frequentist and Bayesian statistical inference.
- Without recombination, data would reflect a single realization of this process, making statistical inference a questionable project.
- The purpose of this paper has been to acquaint cognitive science researchers with some of the core differences between the randomization and normal curve models of statistical inference.
- Second, the sample size is small; hence, the power of statistical inference is potentially limited in this study.
- For the system at hand, likelihood methods may provide a more reliable basis for statistical inference.
- In medical research the periodic calls for a wholesale switch to the use of bayesian statistical inference have been largely ignored.
- This method falls within a novel approach to robust statistical inference from incomplete databases based on probability intervals.
- However, shifting one's means of justification implies a corresponding shift to an alternative model of statistical inference, and in some cases requires moving away from statistically based inference altogether.
- Using the new family of models, we investigate the utility of a variety of new statistical inference procedures.
- The development of theoretical models that can aid in understanding complicated demographic histories and provide a basis for methods of statistical inference has been another major aim of recent work.
- Because these data are not drawn from a random sample, generalizations are based on intuitive plausibility rather than statistical inference.
- We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics.
- In the jargon of statistical inference this means that the relationship between deflation and depression is not ‘statistically significant.’
- Qualitative sampling can be confusing, especially if one's knowledge regarding sampling methods originates from a framework of statistical inference.
- In statistical inference we infer certain facts about a large population - say, the average height of New Yorkers - from measurements made on a sample.
- To that end, statistical inference using Poisson random field models provides powerful likelihood and Bayesian methods for quantifying some of these forces, such as mutation and directional selection.
- His articles are more of a contribution to probability theory than to simultaneous statistical inference, and the reader in search of a convenient reference for such use might prefer.

单词	statistical inference
释义	Definition of statistical inference in English: statistical inference noun mass noun The theory, methods, and practice of forming judgements about the parameters of a population and the reliability of statistical relationships, typically on the basis of random sampling. the problem is fundamental to statistical inference count noun the available data can be used to make statistical inferences Example sentencesExamples Without recombination, data would reflect a single realization of this process, making statistical inference a questionable project. Using the new family of models, we investigate the utility of a variety of new statistical inference procedures. In the jargon of statistical inference this means that the relationship between deflation and depression is not ‘statistically significant.’ For the system at hand, likelihood methods may provide a more reliable basis for statistical inference. The F-ratio associated with the sheep/goat variable should have been 1.57, not 1.33 as reported, although this should not have affected statistical inference. However, for anyone trained in statistical inference and experimental methodology, this will appear as just another blatant attempt to snatch victory out of the jaws of defeat. His articles are more of a contribution to probability theory than to simultaneous statistical inference, and the reader in search of a convenient reference for such use might prefer. However, shifting one's means of justification implies a corresponding shift to an alternative model of statistical inference, and in some cases requires moving away from statistically based inference altogether. The development of theoretical models that can aid in understanding complicated demographic histories and provide a basis for methods of statistical inference has been another major aim of recent work. Other articles written by Savage relate to statistical inference, in particular the Bayesian approach. To that end, statistical inference using Poisson random field models provides powerful likelihood and Bayesian methods for quantifying some of these forces, such as mutation and directional selection. The purpose of this paper has been to acquaint cognitive science researchers with some of the core differences between the randomization and normal curve models of statistical inference. This method falls within a novel approach to robust statistical inference from incomplete databases based on probability intervals. Not only must these models be more realistic, they must also permit fast computation in frequentist and Bayesian statistical inference. Qualitative sampling can be confusing, especially if one's knowledge regarding sampling methods originates from a framework of statistical inference. In statistical inference we infer certain facts about a large population - say, the average height of New Yorkers - from measurements made on a sample. Second, the sample size is small; hence, the power of statistical inference is potentially limited in this study. We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. In medical research the periodic calls for a wholesale switch to the use of bayesian statistical inference have been largely ignored. Because these data are not drawn from a random sample, generalizations are based on intuitive plausibility rather than statistical inference. Definition of statistical inference in US English: statistical inference noun The theory, methods, and practice of forming judgments about the parameters of a population and the reliability of statistical relationships, typically on the basis of random sampling. the problem is fundamental to statistical inference count noun the available data can be used to make statistical inferences Example sentencesExamples The F-ratio associated with the sheep/goat variable should have been 1.57, not 1.33 as reported, although this should not have affected statistical inference. However, for anyone trained in statistical inference and experimental methodology, this will appear as just another blatant attempt to snatch victory out of the jaws of defeat. Other articles written by Savage relate to statistical inference, in particular the Bayesian approach. Not only must these models be more realistic, they must also permit fast computation in frequentist and Bayesian statistical inference. Without recombination, data would reflect a single realization of this process, making statistical inference a questionable project. The purpose of this paper has been to acquaint cognitive science researchers with some of the core differences between the randomization and normal curve models of statistical inference. Second, the sample size is small; hence, the power of statistical inference is potentially limited in this study. For the system at hand, likelihood methods may provide a more reliable basis for statistical inference. In medical research the periodic calls for a wholesale switch to the use of bayesian statistical inference have been largely ignored. This method falls within a novel approach to robust statistical inference from incomplete databases based on probability intervals. However, shifting one's means of justification implies a corresponding shift to an alternative model of statistical inference, and in some cases requires moving away from statistically based inference altogether. Using the new family of models, we investigate the utility of a variety of new statistical inference procedures. The development of theoretical models that can aid in understanding complicated demographic histories and provide a basis for methods of statistical inference has been another major aim of recent work. Because these data are not drawn from a random sample, generalizations are based on intuitive plausibility rather than statistical inference. We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. In the jargon of statistical inference this means that the relationship between deflation and depression is not ‘statistically significant.’ Qualitative sampling can be confusing, especially if one's knowledge regarding sampling methods originates from a framework of statistical inference. In statistical inference we infer certain facts about a large population - say, the average height of New Yorkers - from measurements made on a sample. To that end, statistical inference using Poisson random field models provides powerful likelihood and Bayesian methods for quantifying some of these forces, such as mutation and directional selection. His articles are more of a contribution to probability theory than to simultaneous statistical inference, and the reader in search of a convenient reference for such use might prefer.
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