Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Intervalo de Confianza× | Potencia estadística y tamaño de muestra× | |
|---|---|---|
| Campo | Estadística para la investigación | Estadística para la investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1937 | 1988 |
| Autor original≠ | Jerzy Neyman | Jacob Cohen |
| Tipo | Concept | Concept |
| Fuente seminal≠ | Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 |
| Alias | CI, 95% CI, credible interval, interval estimate | power analysis, sample size calculation, 1 minus beta, sensitivity |
| Relacionados | 4 | 4 |
| Resumen≠ | A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results. | Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design: underpowered studies produce inflated effect size estimates and are unlikely to replicate. The standard benchmark is 80% power (β = 0.20), though critical studies may require 90% power. |
| ScholarGateConjunto de datos ↗ |
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