What does resampling mean?
Resampling is the practice of drawing additional or replacement samples from the same source population or data distribution. In statistics, it is used to assess variability, estimate uncertainty, and test whether a result remains stable under different samples; bootstrap methods are a familiar example. In data science and machine learning, resampling can mean selecting or replacing observations to address sparse classes, reduce duplication, or create training sets that better represent the intended population. The term is also used in signal processing when an existing signal or set of observations is sampled again. Its general implication is repetition with an analytical purpose: rather than simply collecting new data, the practitioner uses the same source more than once to examine robustness, improve balance, or reduce the influence of particular observations.
nounThe process of drawing a new sample from the same population or source distribution, often to estimate variability or improve the reliability of an analysis. It may also refer to the repeated selection or replacement of observations within an existing data set.
- The process of drawing a new sample from the same population or source distribution.
- The replacement or rearrangement of observations within an existing data set for analytical purposes.
"Bootstrap resampling helped the researchers quantify the uncertainty surrounding their estimate."
"The team used resampling to test whether the pattern persisted across different draws."
"Resampling the records allowed the model to train on a more balanced distribution of examples."
The plural is grammatically possible but uncommon, since resampling usually refers to an uncountable process.
"The researchers compared several resamplings of the original data set."
Resampling turns one dataset into many possible versions, helping statisticians see how stable their conclusions really are.
Reviewed by Deb Chak, Editor. AI-assisted content curated by RJS Tech Solutions LLP.
Where does resampling come from?
The word is formed from “re-” and “sample,” with the suffix “-ing” indicating the activity or process. “Sample” entered English through Anglo-French and ultimately derives from Latin exemplum, meaning “example” or “model.” Related words include “sampling” and “exemplar,” while “exemplary” continues the same Latin root with a different formation.
Related word forms
How resampling is actually used
The term is especially common in statistics, data science, signal processing, and machine learning. In statistical analysis, it normally conveys repetition intended to measure uncertainty, robustness, or generalization rather than the collection of an entirely new independent data set.
Easily confused with resampling
Sampling is the general act of selecting observations from a population, whereas resampling usually means repeating that selection or replacing observations with a new sample from the same source.