Bootstrapping is a statistical method that helps to estimate the variability of a statistic by creating numerous re-sampled versions of a dataset, and is especially handy with small sample sizes. Essentially, it involves repeatedly drawing samples, with replacement, from a given dataset, and calculating a statistic (e.g., mean, median) or model parameter for each sample. This is done thousands of times to build a distribution of the statistic, which can then be analyzed to estimate its standard error, confidence intervals, and other properties. In model development, bootstrapping aids in understanding and reducing variability and bias in predictions, enhancing model stability and reliability. By repeatedly training and validating models on different bootstrap samples, we gain insights into the model’s robustness and generalizability, allowing for informed statistical inferences without additional data collection. This technique serves as a practical tool for exploring sample space and deriving meaningful statistical insights when dealing with limited data.
I plan to apply a specific technique to our data in order to estimate the sampling distribution, with the aim of investigating whether this approach will enhance model stability.