The Fisher information is a way of measuring the amount of information that an observable random variable "X" carries about an unknown parameter "θ" upon which the probability of "X" depends. Let "f"("X"; "θ") be the probability density function (or probability mass function) for "X" conditioned on the value of "θ". It describes the probability that we observe a given outcome of "X", "given" a known value of "θ". If "f" is sharply peaked with respect to changes in "θ", it is easy to indicate the "correct" value of "θ" from the data, or equivalently, that the data "X" provides a lot of information about the parameter "θ". If the likelihood "f" is flat and spread-out, then it would take many samples of "X" to estimate the actual "true" value of "θ" that "would" be obtained using the entire population being sampled. This suggests studying some kind of variance with respect to "θ".