Bootstrap Sampling

Bootstrap sampling is a statistical method used to estimate the characteristics of a larger population by repeatedly taking samples from a smaller dataset. Imagine having a small set of data points and wanting to understand the overall trend or variability. By randomly selecting data points from this set—sometimes choosing the same point more than once—you create new "resampled" datasets. Analyzing these repetitive samples helps assess the reliability of results, calculate confidence intervals, and understand uncertainty without needing new data from the entire population. It's a powerful technique for making inferences from limited data.