Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Neatkarīgo paraugu t-tests× | Maksimālās vergojamošās korelācijas novērtēšana× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1908 | 1922 |
| Autors≠ | Student (W. S. Gosset) | R. A. Fisher |
| Tips≠ | Parametric mean comparison | Parametric point estimator |
| Pirmavots≠ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ | Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368. DOI ↗ |
| Citi nosaukumi | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi | MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances. | Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures. |
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