The math underpinning the project: correlation time and effective sample size, the 95th-percentile ECDF for accuracy requirements, drift-slope confidence intervals, peak-and-snapback inference of detection time, and the 27/30 compliance rule.
## What you'll learn 1. Explain why correlated time-series data require **effective sample size** $N_{\text{eff}}$, not the raw sample count $N$. 2. Estimate $T_{\text{corr}}$ from an autocorrelation function and apply the **10% rule of thumb**. 3. Derive $N_{\text{eff}}$ from first principles and plan a statistically defensible test duration. 4. Apply the **95th-percentile ECDF** for accuracy requirements (rather than a CI on the mean). 5. Estimate inertial **drift rate** with a 95% confidence interval on the slope. 6. Infer **fault detection time** from navigation-error behavior when no internal fault flag is available. ## In this block