Data Analysis

While the human brain is powerful tool for quickly recognizing patterns in data, it will frequently make errors in interpreting random data. Luckily, these mistakes occur in systematic and predictable ways. Statistical models provide an analytical framework that helps us avoid these error-prone heuristics and draw accurate conclusions from random data. This non-technical presentation highlights some tricks of the trade learned by studying data and the way the human brain processes....

Bootstrapping is a powerful nonparametric tool for conducting statistical inference with many applications to data from operational testing. Bootstrapping is most useful when the population sampled from is unknown or complex or the sampling distribution of the desired statistic is difficult to derive. Careful use of bootstrapping can help address many challenges in analyzing operational test data. Suggested Citation Avery, Matthew R. A First Step into the Bootstrap World. IDA Document NS D-5816....

For many systems, testing is expensive and only a few live test events are conducted. When this occurs, testers frequently use a model to extend the test results. However, testers must validate the model to show that it is an accurate representation of the real world from the perspective of the intended uses of the model. This raises a problem when only a small number of live test events are conducted, only limited data are available to validate the model, and some testers struggle with model validation....