Resampling techniques are statistical methods used to draw repeated samples from a dataset to assess the variability of a statistic. Common methods include bootstrapping, where samples are drawn with replacement, and cross-validation, which involves partitioning data into subsets to evaluate model performance. These techniques help improve the reliability of estimates and model assessments. These methods are particularly useful in situations with limited data, allowing researchers to make inferences about a population without needing to collect more data. By simulating the sampling process, resampling techniques provide insights into the stability and accuracy of statistical estimates and predictions.