Bootstrap resampling is a statistical method used to estimate the uncertainty, variability, or confidence intervals of a model or parameter by repeatedly sampling (with replacement) from the original dataset and calculating the desired statistic on each resampled dataset.
It is especially useful in battery research and engineering when:
The available data is limited
You want to assess the robustness of machine learning models
You need to quantify prediction intervals in battery aging, degradation, or life expectancy models
