What is the difference between an m-failure test and a 0-failure test?

In an m-failure test plan, the reliability demonstration test is successful if no more than m failures occur. The most common m-failure tests are the 0-failure test (m=0) or the 1-failure test (m=1).

For example, suppose you are testing lawn mower motors using an m-failure test where m = 3. Your reliability test is successful if no more than 3 failures occur in n identical systems that are tested independently and are subject to the same testing duration. Should more than 3 failures occur, your reliability test fails and the system doesn't meet the reliability requirement that you want to prove.

Planning for an m-failure test includes determining both the sample size and testing duration to maximize the chance of passing the reliability test and proving your reliability requirements. When choosing between a 0-failure test plan and an m-failure (m>0) test plan consider the following:

	0-failure test plan	m-failure test plan (m>0)
Total test time	May reduce total test time for highly reliable items.	May reduce total test time if you can run the tests sequentially. For example, if you are testing 3 units in a 1-failure test and the first 2 units pass, you do not have to test the third.
Practicality	Is not practical when you are likely to have at least one failure.	May not be feasible for highly reliable units. Has a better chance of passing than a 0-failure test when you have a marginally improved design.
Verification of assumptions	Does not let you verify the assumptions of the test design: You cannot estimate the shape (Weibull distribution) or scale (other distributions) to compare it to the assumed value. You can estimate the scale (Weibull or exponential distribution) or location (other distributions), but your estimate may be conservative.	Lets you verify the assumptions of the test design. There are several assumptions that you should consider when using an m-failure test plan: For the Weibull distribution, you know the shape parameter and want to demonstrate the scale parameter. For the exponential distribution, you want to demonstrate the scale parameter. The shape parameter is one. For the extreme value, normal, lognormal, logistic, and loglogistic distributions, you know the scale parameter and want to demonstrate the location parameter.