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Bootstrapping (statistics) Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model estimated from the data. [1] Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. [2][3] This technique ...
Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined ...
The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics. [1] [2] The theory covers approaches to statistical-decision problems and to statistical inference, and the actions and deductions that satisfy the basic principles stated for these different approaches.
Statistics (from German: Statistik, orig. "description of a state, a country") [1][2] is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. [3][4][5] In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a ...
The foundations of statistics are the mathematical and philosophical bases for statistical methods. These bases are theoretical frameworks that ground and justify methods of statistical inference, estimation, hypothesis testing, uncertainty quantification, and the interpretation of statistical conclusions. Further, a foundation can be used to ...
Sampling (statistics) In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population and statisticians ...
Administering one form of the test to a group of individuals. At some later time, administering an alternate form of the same test to the same group of people. Correlating scores on form A with scores on form B. The correlation between scores on the two alternate forms is used to estimate the reliability of the test.
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]