Compara mètodes

	Correlació vs Causalitat	Proves d'Hipòtesi Nul·la
Camp	Estadística per a la recerca	Estadística per a la recerca
Família	Process / pipeline	Process / pipeline
Any d'origen	1965	1925
Autor original	Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)	Ronald Fisher; Neyman & Pearson
Tipus	Concept	Concept
Font seminal	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0-521-89560-6	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Àlies	correlation and causation, causal inference, spurious correlation, confounding	NHST, hypothesis formulation, null hypothesis, alternative hypothesis
Relacionats	4	4
Resum	Correlation measures the strength and direction of association between two variables; causation implies that changes in one variable directly produce changes in another. A strong correlation (e.g., r = 0.9) does not prove causation. Classic examples abound: shoe size and reading ability are correlated in children (confounded by age), but shoe size does not cause reading ability. Understanding when correlation implies causation requires evaluating study design, confounding variables, temporal precedence, and mechanism. Randomized experiments offer the strongest causal evidence; observational studies must carefully control for confounders.	Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.