  Point estimators may not exist, need not be unique, and their distributions
are not parameter invariant. Generalized estimators provide distributions that
are parameter invariant, unique, and exist when point estimates do not.
Comparing point estimators using variance is less useful when estimators are
biased. A squared slope $\Lambda$ is defined that can be used to compare both
point and generalized estimators and is unaffected by bias. Fisher information
$I$ and variance are fundamentally different quantities: the latter is defined
at a distribution that need not belong to a family, while the former cannot be
defined without a family of distributions, $M$. Fisher information and
$\Lambda$ are similar quantities as both are defined on the tangent bundle
$T\!M$ and $I$ provides an upper bound, $\Lambda\le I$, that holds for all
sample sizes -- asymptotics are not required. Comparing estimators using
$\Lambda$ rather than variance supports Fisher's claim that $I$ provides a
bound even in small samples. $\Lambda$-efficiency is defined that extends the
efficiency of unbiased estimators based on variance. While defined by the
slope, $\Lambda$-efficiency is simply $\rho^{2}$, the square of the correlation
between estimator and score function.
