Hypothesis Testing of Failure Time Data: Part I

How can you tell if a design change actually improved a product's reliability? Is the new component from Vendor B truly more durable than the one from Vendor A? These are critical business questions that require more than just a gut feeling, they demand rigorous statistical testing.

This article provides a practical, hands-on tutorial on how to use the log-rank test to statistically compare failure rates between different groups. Using real-world datasets and step-by-step R code, you'll learn how to perform non-parametric hypothesis tests on time-to-failure data. More importantly, this guide also highlights a critical lesson in data science: the importance of verifying your results and navigating potential pitfalls in software packages to ensure your conclusions are sound.