Optimized parameters lie in minima of plots of 2  vs. parameter values. For a single parameter problem, if a minimum is steep, characterized by a relatively high second derivative, then the parameter can be considered well-defined, and the information content of data concerning that parameter is large. When a minimum is shallow, characterized by a relatively low second derivative, then the parameter can be considered poorly defined, and the information content of the data concerning that parameter is small. For a single parameter, the Fisher Information I is defined as the second derivative of 2  with respect to that parameter. Since the variance in the distribution of a parameter value will be low if it lies in a steep minimum, it is reasonable that the reciprocal of the Fisher information gives the parameter variance.