Selecting an appropriate distribution is an essential first step in performing reliability analyses. If the selected distribution does not fit the data well, then the reliability estimates will be inaccurate. A well-fitting distribution model is also needed in order to extrapolate beyond the range of data. Consider the following criteria when choosing the most appropriate distribution for your reliability data:

- Use engineering and historical knowledge of the situation. For example, do the data follow a symmetric distribution? Is the hazard constant, increasing, or decreasing? What distribution has worked historically for similar situations?
- Perform a distribution analysis and use probability plots to compare the candidate distributions or to assess the appropriateness of the chosen distribution.
- Evaluate the Anderson-Darling goodness-of-fit statistic and the Pearson correlation coefficient:
- Substantially lower values of Anderson-Darling generally indicate a better fitting distribution. The Anderson-Darling statistic is calculated for both the maximum likelihood estimation method (MLE) and the least squares estimation method (LSE).
- Substantially higher values of the Pearson correlation coefficient identify a better fitting distribution. The correlation coefficient is available for the LSE method.

- Evaluate how different distributions affect your conclusions:
- If several distributions provide an adequate fit to the data and result in similar conclusions, then it probably does not matter which distribution you choose.
- If your conclusions depend on the distribution that you choose, you may want to report the most conservative conclusion or collect more information.

Frequently, you can model a set of data with more than one distribution, or with a distribution that has one, two, or three parameters. For example, for each type of data, several distributions may be fit:

- Right-skewed data
- Often, you can fit either the Weibull or the lognormal distribution and obtain a good fit to the data.
- Symmetric data
- Often, you can fit the Weibull or the lognormal distribution. Sometimes, you can fit the normal distribution (depending on the heaviness of the tails) and obtain similar results.
- Left-skewed data
- Often, you can fit the Weibull or the smallest extreme value distribution.

For more information on specific distributions that are used to model reliability data, go to the following topics: