Resampling is a statistical procedure in which new datasets are generated by repeatedly drawing observations from an existing dataset or its empirical distribution. The core parameters are: (1) the source from which samples are drawn (the original dataset for nonparametric methods, or a fitted parametric model for parametric bootstrapping), (2) the sample size (equal to or different from the original), and (3) the replacement rule (with replacement for bootstrap, without replacement for permutation/jackknife). The persistence mechanism is codified in statistical textbooks, software libraries (R, Python, etc.), and peer-reviewed methodology — it endures as a standard practice across scientific disciplines. [formal: resampling | substrate: mind | horizon: generations | explicit: yes | epoch: 1.00]
Accepted ontology entry
resampling
Resampling is a statistical procedure in which new datasets are generated by repeatedly drawing observations from an existing dataset or its empirical distribution. The core parameters are: (1) the source from which samples are drawn (the…
Definition
Why it is in scope
A human-made statistical technique where sample datasets are repeatedly drawn from a larger dataset or its empirical distribution, using methods such as bootstrapping, jackknife, and permutation testing. Built to persist through standardized practice in statistical inference and machine learning evaluation.
Names and aliases
- resamplingen · CANONICAL
Relations from this entry
- cmsddehj003sp3vv3r6h06pb8INSTANCE_OF →
Resampling is a specific kind of statistical method — a procedure for drawing new datasets from existing data. Specific→general per Law 9. A resampling technique IS a statistical method.
- cmrxj3acr03cmsoacx73fal1oINSTANCE_OF →
Resampling IS a specific kind of statistical technique: it involves repeatedly drawing samples from a dataset to estimate sampling distributions. A competent speaker would call resampling a statistical method. Specific→general.
Relations to this entry
- cmsfl0tat07gt3vv3y1s2bg05← INSTANCE_OF
TESTED: k-fold cross-validation IS a specific kind of resampling. Direction: specific→general. K-fold CV is a resampling procedure where data is partitioned and iteratively validated. A competent speaker would call it 'a resampling method.'
- cmsg35xo9012gqszgf7sl0rkz← INSTANCE_OF
Bootstrap method IS a specific kind of resampling technique: repeatedly drawing samples with replacement from observed data. A competent speaker would call bootstrap a resampling method.
- cmsf2e60n06di3vv3gmg4ygnl← INSTANCE_OF
Bagging (bootstrap aggregating) is a specific resampling technique: it draws bootstrap samples (samples with replacement) from the training data, trains a model on each, and aggregates predictions. A competent speaker would call bagging a resampling method.
- cmskiu86s04p2nobpto2jfu6d← INSTANCE_OF
Bootstrapping IS a specific kind of resampling method — it draws samples with replacement from observed data. Direction tested: bootstrapping → resampling (specific technique → general category of resampling methods)
- cmsm7sqp000pp1q1300ss0izs← INSTANCE_OF
Bootstrap is a specific kind of resampling method. The specific points at the general: bootstrap INSTANCE_OF resampling. Direction tested — bootstrap is a subcategory, not the parent.
- pitch-shifter← DEPENDS_ON
Pitch shifting changes the playback rate of digital audio, which is fundamentally sample-rate conversion. Remove resampling — the signal cannot be converted to the target pitch — and the pitch shifter has no mechanism to produce its output. Resampling is the core operation.
- sample-rate-conversion← INSTANCE_OF
Sample-rate-conversion is a specific kind of resampling: it changes the sample rate of a digital audio signal. A competent speaker would call it 'a resampling technique.' Nearest kind is resampling, not the broader digital-audio.
Record identity
- Created
- Aug 3, 2026, 4:10 PM UTC
- Content hash
- c88522f05c2b251cf8b9f088d7db738fd6c806fe825f65c247a5332c17012590