Bootstrapping
The short answer · 19 words
In short
Statistical resampling, in which, in the classical non-parametric case, repeated samples are taken with traverse from the existing observations.
Flow-alerta.cab (2026): Bootstrapping. https://flow-alerta.cab/en/glossar/bootstrapping. Editorially reviewed on: 09 October 2026.
How much detail would you like?
Practical
Useful if the uncertainty of a metric is difficult to analytically determine. The method also forms the basis for methods such as bagging.
Example scenario: For the estimated model quality, many bootstrap samples are drawn from the existing data set and the dispersion of the results is evaluated.
Use for uncertainty estimation, robust key figures and model analyses if the data structure allows meaningful resampling.
At a glance
- Core
- In the classical non-parametric bootstrap, the observed sample is used as an approximation for the unknown population. From this, many new samples of the same size are drawn. The distribution of a parameter calculated therefrom can be used to estimate uncertainty, standard errors or confidence intervals.
- Use
- Useful if the uncertainty of a metric is difficult to analytically determine. The method also forms the basis for methods such as bagging.
- Example
- Example scenario: For the estimated model quality, many bootstrap samples are drawn from the existing data set and the dispersion of the results is evaluated.
- Limits
- Equate bootstrapping with bagging; resampling dependent data unchecked as independent observations; treating very small or distorted samples as representative.
In context
Questions about this term
Is it pulled back?
In the classic bootstrap, yes.
Is Bootstrap the Same as Cross-Validation?
No.
Can Bootstrap repair bad initial data?
No.
All details and background
In the classical non-parametric bootstrap, the observed sample is used as an approximation for the unknown population. From this, many new samples of the same size are drawn. The distribution of a parameter calculated therefrom can be used to estimate uncertainty, standard errors or confidence intervals.
ELI5 – Explained simply
You create many slightly different data sets by selecting randomly from your existing data over and over again.
Why it matters in practice
Useful if the uncertainty of a metric is difficult to analytically determine. The method also forms the basis for methods such as bagging.
Example
Example scenario: For the estimated model quality, many bootstrap samples are drawn from the existing data set and the dispersion of the results is evaluated.
Pitfalls and limits
- Equate bootstrapping with bagging; resampling dependent data unchecked as independent observations; treating very small or distorted samples as representative.
Origin & history
In 1979, Bradley Efron published the article “Bootstrap Methods: Another Look at the Jackknife” in the Annals of Statistics. Current libraries such as SciPy implement the procedure for estimating statistical uncertainty.
Use in mid-sized companies
- Use for uncertainty estimation, robust key figures and model analyses if the data structure allows meaningful resampling.
Related terms
- Bagging
- Cross validation
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View use casesUse Case: Uncertainty estimation of a model or business metric by resampling.Sources
- doi.orgEfron, Bootstrap Methods: Another Look at the Jackknife (1979)
- www.jstor.orgOriginalveröffentlichung, Annals of Statistics 7(1)
- docs.scipy.orgSciPy, bootstrap
Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: