Resampling techniques

Resampling techniques are methods used to assess the accuracy or stability of statistical models by repeatedly drawing samples from the data. Think of it like testing a recipe multiple times with different ingredient combinations to see how consistent the results are. Common methods include bootstrapping, where you randomly pick data points with replacement to create new samples, and cross-validation, which splits data into parts to train and test models repeatedly. These techniques help ensure that findings or predictions are reliable and not just specific to a particular dataset.