This paper reviews the fundamentals of prognostics with emphasis on the estimation of remaining life and the interrelationships between accuracy, precision and confidence. A distinction is made between the static view of failure distributions derived from historical data and the dynamic view of remaining life derived from condition. The nonstationary nature of prognoses is illustrated using data from a failing SH-60 helicopter gearbox. A method is demonstrated that measures the accuracy and uncertainty of remaining life estimates using example prognostic features. This method isolates the uncertainty attributable to features and their interpretation from the uncertainty due to the random variables that govern the physics of component failure. Results from the example features support a hypothesized trend of improved accuracy and lower uncertainty as remaining life decreases.