## Sufficiency Principle

A statistic is **sufficient** to a statistical model and its associated unknown parameter,
if no other statistic that can be calculated from the same sample
provides any additional information as to the value of the parameter. [@Fisher1922]

Notes on Sufficiency principle

## Conditionality Principle

Informally, the conditionality principle can be taken as the claim that
experiments which were not actually performed are statistically irrelevant.

Conditionality Principle:
If $E$ is any experiment having the form of a mixture of component experiments $E_h$,
then for each outcome $(E_h, x_h)$ of $E$,
[...] the evidential meaning of any outcome $x$ of any mixture experiment $E$
is the same as that of the corresponding outcome $x_h$ of the corresponding component experiment $E_h$,
ignoring the over-all structure of the mixed experiment. [@Birnbaum1962]

## Likelihood Principle

Although the relevance of the proof to data analysis remains controversial among statisticians,
many Bayesians and likelihoodists consider the likelihood principle foundational
for statistical inference.

🏷 Category=Statistics