In reliability, the survival probability is the proportion of units that survive beyond a specified time. These estimates of survival probabilities are frequently referred to as reliability estimates. Use these values to determine whether your product meets reliability requirements or to compare the reliability of two or more designs.
For example, a mobile phone manufacturer is studying the reliability of a component part in an accelerated life test. The survival probability at 70 hours is 0.197736. This means that at 70 hours, approximately 19.77% of these parts will have not yet failed.
In probit analysis, survival probabilities estimate the proportion of units that survive at a certain stress level.
For example, a reliability engineer exposed light bulbs to various voltages and recorded whether or not the bulb burned out before 800 hours. The engineer performed a probit analysis to estimate the survival probability for light bulbs subjected to 117 volts and determined that the probability of a bulb surviving longer than 800 hours is 0.7692 at 117 volts.
The cumulative failure probabilities are the likelihood of failing instead of surviving. In the light bulb example, the probability of failing before 800 hours at 117 volts is 1 - 0.7692 = 0.2308.