En estadística, el lema fonamental de Neyman-Pearson és un resultat que descriu el criteri òptim per distingir dues hipòtesis simples H0: θ=θ0 i H1: θ=θ1.

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  • En estadística, el lema fonamental de Neyman-Pearson és un resultat que descriu el criteri òptim per distingir dues hipòtesis simples H0: θ=θ0 i H1: θ=θ1.
  • In statistics, the Neyman–Pearson lemma, named after Jerzy Neyman and Egon Pearson, states that when performing a hypothesis test between two point hypotheses H0: θ = θ0 and H1: θ = θ1, then the likelihood-ratio test which rejects H0 in favour of H1 whenwhereis the most powerful test of size α for a threshold η. If the test is most powerful for all , it is said to be uniformly most powerful (UMP) for alternatives in the set .In practice, the likelihood ratio is often used directly to construct tests — see Likelihood-ratio test. However it can also be used to suggest particular test-statistics that might be of interest or to suggest simplified tests — for this, one considers algebraic manipulation of the ratio to see if there are key statistics in it related to the size of the ratio (i.e. whether a large statistic corresponds to a small ratio or to a large one).
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  • En estadística, el lema fonamental de Neyman-Pearson és un resultat que descriu el criteri òptim per distingir dues hipòtesis simples H0: θ=θ0 i H1: θ=θ1.
  • In statistics, the Neyman–Pearson lemma, named after Jerzy Neyman and Egon Pearson, states that when performing a hypothesis test between two point hypotheses H0: θ = θ0 and H1: θ = θ1, then the likelihood-ratio test which rejects H0 in favour of H1 whenwhereis the most powerful test of size α for a threshold η.
rdfs:label
  • Lemme de Neyman-Pearson
  • Lema de Neyman-Pearson
  • Lema de Neyman-Pearson
  • Lemma fondamentale di Neyman-Pearson
  • Neyman-Pearson-Lemma
  • Neyman–Pearson lemma
  • ネイマン・ピアソンの補題
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