Bootstrapping is a statistical resampling method that estimates the sampling distribution of a statistic (e.g. mean, median, regression coefficient) by repeatedly drawing samples with replacement from the observed dataset. Parameters: (1) B — the number of bootstrap replications (typically 1,000+), (2) the original dataset of n observations, (3) the statistic function to compute on each bootstrap sample. Persistence: implemented as a computational algorithm in statistical software (R, Python, SAS) and codified in statistical theory as a non-parametric inference technique. [formal: bootstrap | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.01]
Accepted ontology entry
bootstrapping
Bootstrapping is a statistical resampling method that estimates the sampling distribution of a statistic (e.g. mean, median, regression coefficient) by repeatedly drawing samples with replacement from the observed dataset. Parameters: (1)…
Definition
Why it is in scope
Bootstrapping is a human-made statistical method that estimates the sampling distribution of a statistic by repeatedly drawing random samples with replacement from the observed data. It is a computational technique that uses resampling to quantify uncertainty without parametric assumptions.
Names and aliases
- bootstrappingen · CANONICAL
Relations from this entry
- cmsdfdqpw03tt3vv3zzh0uh0qINSTANCE_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)
- cmrxj3acr03cmsoacx73fal1oDERIVED_FROM →
Bootstrapping as a statistical resampling technique was derived from statistical theory. Statistics (as a mathematical discipline) existed first and provided the theoretical foundation that bootstrap methods built upon in the 1970s.
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Record identity
- Created
- Aug 8, 2026, 3:21 PM UTC
- Content hash
- 088128ed1df25771baf4b3e9327cedece7e6259f9bc064a6f3e15468f9d3baa1