median time to failure

**Median time to failure** is the **lifetime point where half of the population has failed and half remains operational** - it is a robust central tendency metric that is often easier to interpret than mean lifetime in skewed failure distributions. **What Is Median time to failure?** - **Definition**: Time t50 such that cumulative failure probability reaches 0.5. - **Robustness**: Less sensitive to extreme long-life outliers than MTTF in heavy-tail datasets. - **Model Link**: Directly derived from fitted CDF or nonparametric survival estimates. - **Use Context**: Commonly reported in accelerated stress studies and comparative technology benchmarking. **Why Median time to failure Matters** - **Clear Communication**: Median life is intuitive for technical and non-technical stakeholders. - **Skewed Data Stability**: Provides stable center estimate when failure-time distribution is asymmetric. - **Experiment Comparison**: Useful for ranking process splits without overemphasizing tail noise. - **Qualification Insight**: Differences between median and mean life reveal distribution skew and tail behavior. - **Decision Support**: Helps evaluate whether central reliability performance meets program expectations. **How It Is Used in Practice** - **Curve Estimation**: Build survival or cumulative curves from test data with proper censoring handling. - **Point Extraction**: Interpolate time at 50 percent failure or 50 percent survival crossing. - **Confidence Quantification**: Compute interval bounds to reflect sampling uncertainty around t50. Median time to failure is **a practical and robust lifetime anchor for comparative reliability analysis** - it captures central durability without being dominated by rare outlier behavior.