Comparar métodos

	Inferencia de Aleatorización Exacta de Fisher	Regresión por Mínimos Cuadrados Ordinarios (MCO)
Campo	Estadística	Econometría
Familia	Regression model	Regression model
Año de origen	1935	2019
Autor original	Ronald A. Fisher	Wooldridge (textbook treatment); classical least squares
Tipo	Exact permutation-based inference	Linear regression
Fuente seminal	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	5	5
Resumen	Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).