Jackknife Resampling is a statistical technique used to estimate the bias and variance of a sample statistic. It involves systematically leaving out one observation at a time from the dataset and calculating the statistic of interest for each subset. This process helps in understanding how much the statistic varies with different samples. The main advantage of Jackknife Resampling is its simplicity and ease of implementation. It provides a way to assess the stability of a statistic without needing to create multiple new samples, making it particularly useful in small datasets where traditional resampling methods, like Bootstrap, may not be as effective.