Data considerations for Parametric Distribution Analysis (Arbitrary Censoring)

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data that you collect are usually failure times

For example, you might collect failure times for units that are tested at a specific temperature. You might also collect samples of failure times under different temperatures, or under different combinations of stress variables. Alternatively, the data might measure product usage in units other than time, such as the amount of mileage for a tire until it fails.

Your data must be arbitrarily censored

To be considered arbitrarily censored, your data should include either left-censored observations (you know only the time before which the failure occurred) or interval-censored observations (you know only the times between which the failure occurred). Your data could also have a varied censoring scheme that includes exact failure times, right censoring, left censoring, and interval censoring. However, if your data consist of only exact failure times and/or right-censored observations (you know only the time after which the failure occurred), use Parametric Distribution Analysis (Right Censoring). For more information on censored data, go to Data censoring.

To evaluate each cause of failure separately, record failure modes

A failure mode indicates the cause of a failure. Because different failure modes often have different failure distributions, consider grouping the data by failure mode, when possible. To obtain results for each failure mode, as well as for the overall system, record the cause of each system failure in a worksheet column. For more information, go to What is a failure mode?.

The distribution that you select must fit your data adequately

If the selected distribution does not fit your data well, the estimates of the percentiles, failure probabilities, and survival probabilities will not be accurate. To determine which parametric distribution best fits your data, use Distribution ID Plot (Arbitrary Censoring). If no parametric distribution fits your data adequately, use Nonparametric Distribution Analysis (Right Censoring) instead.