Materials scientists and engineers are making increased use of statistical analyses in interpreting S-N and ε-N fatigue data. Statistical analysis applies when the given data can be reasonably assumed to be a random sample of (or representation of) some specific defined population or universe of material of interest (under specific test conditions), and it is desired either to characterize the material or to predict the performance of future random samples of the material (under similar test conditions), or both.

1.1 This practice covers only S-N and ε-N relationships that may be reasonably approximated by a straight line (on appropriate coordinates) for a specific interval of stress or strain. It presents elementary procedures that presently reflect good practice in modeling and analysis. However, because the actual S-N or ε-N relationship is approximated by a straight line only within a specific interval of stress or strain, and because the actual fatigue life distribution is unknown, it is not recommended that (a) the S-N or ε-N curve be extrapolated outside the interval of testing, or (b) the fatigue life at a specific stress or strain amplitude be estimated below approximately the fifth percentile (P ≃ 0.05). As alternative fatigue models and statistical analyses are continually being developed, later revisions of this practice may subsequently present analyses that permit more complete interpretation of S-N and ε-N data.