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In statistics, the score, score function, efficient score[1] or informant[2] indicates how sensitive a likelihood function {\displaystyle {\mathcal {L}}(\theta ;X)} {\displaystyle {\mathcal {L}}(\theta ;X)} is to its parameter {\displaystyle \theta } \theta . Explicitly, the score for {\displaystyle \theta } \theta is the gradient of the log-likelihood with respect to {\displaystyle \theta } \theta .

The score plays an important role in several aspects of inference. For example:

in formulating a test statistic for a locally most powerful test;[3]
in approximating the error in a maximum likelihood estimate;[4]
in demonstrating the asymptotic sufficiency of a maximum likelihood estimate;[4]
in the formulation of confidence intervals;[5]
in demonstrations of the Cramér–Rao inequality.[6]
The score function also plays an important role in computational statistics, as it can play a part in the computation of maximum likelihood estimates

The score is the gradient (the vector of partial derivatives), with respect to some parameter {\displaystyle \theta } \theta , of the logarithm (commonly the natural logarithm) of the likelihood function (the log-likelihood). If the observation is {\displaystyle X} X and its likelihood is {\displaystyle {\mathcal {L}}(\theta ;X)} {\displaystyle {\mathcal {L}}(\theta ;X)}, then the score {\displaystyle V} V can be found through the chain rule:
Thus the score {\displaystyle V} V indicates the sensitivity of {\displaystyle {\mathcal {L}}(\theta ;X)} {\displaystyle {\mathcal {L}}(\theta ;X)} (its derivative normalized by its value). Note that {\displaystyle V} V is a function of {\displaystyle \theta } \theta and the observation {\displaystyle X} X, so that, in general, it is not a statistic. However in certain applications, such as the score test, the score is evaluated at a specific value of {\displaystyle \theta } \theta (such as a null-hypothesis value, or at the maximum likelihood estimate of {\displaystyle \theta } \theta ), in which case the result is a statistic.

In older literature, the term “linear score” may be used to refer to the score with respect to infinitesimal translation of a given density. This convention arises from a time when the primary parameter of interest was the mean or median of a distribution. In this case, the likelihood of an observation is given by a density of the form {\displaystyle {\mathcal {L}}(\theta ;X)=f(X+\theta )} {\displaystyle {\mathcal {L}}(\theta ;X)=f(X+\theta )}. The “linear score” is then defined as

It is worth restating the above result in words: the expected value of the score is zero. Thus, if one were to repeatedly sample from some distribution, and repeatedly calculate the score, then the mean value of the scores would tend to zero as the number of repeat samples approached infinity.
Note that the Fisher information, as defined above, is not a function of any particular observation, as the random variable {\displaystyle X} X has been averaged out. This concept of information is useful when comparing two methods of observation of some random process.

We can now verify that the expectation of the score is zero. Noting that the expectation of A is nθ and the expectation of B is n(1 − θ) [recall that A and B are random variables], we can see that the expectation of V is