AI engineering glossary · Statistics inference
What is jackknife?
Jackknife: A resampling method that recomputes a statistic n times, each time leaving out one observation, and uses the variation among results to estimate bias and standard error. It is older and simpler than the bootstrap.
Jackknife: an example
For 30 observations, compute the median 30 times with each observation left out in turn.
Related terms
More Statistics inference terms
- Kolmogorov-Smirnov test
- Likelihood function
- Likelihood ratio test
- Log-likelihood
- Mann-Whitney U test
- Margin of error
- Maximum a posteriori estimation
- Minimum detectable effect
Where you learn it
Jackknife is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.