Bootstrapping
Bootstrapping - Related Videos
01:24
Bootstrapping
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2025
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
05:37
An R-Based Landscape Validation of a Competing Risk Model
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Cited by 2 •
2022
The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of it.
10:23
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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2025
The protocol presented here aims to guide the users on the computer-based processing of cytochrome c oxidase subunit I (COI) gene sequences generated from beetles, such that the species clustering hypothesis called molecular operational taxonomic units (MOTUs) can be generated from DNA.
08:55
Application of DNA Barcoding to Identify Medicinal Plants
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Cited by 2 •
2024
The present protocol describes the use of DNA barcoding technology to authenticate plant-based medicinal materials, and the medicinal plant Angelica sinensis (Oliv.) Diels was identified as an example.
07:17
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
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Cited by 1 •
2025
This protocol describes how to use the MEDUSA analytical method to quantify the death regulatory effect of each gene knockout. It includes instructions on determining experimental conditions that optimize sensitivity and a step-by-step tutorial on performing the analysis.
FAQs
Sampling with replacement allows each resample to differ from the observed dataset while retaining the dataset as the source of information. Repeating the calculation across these resamples produces a sampling distribution for the chosen statistic. The spread and shape of that distribution provide evidence about how variable the sample-based result may be.
Bootstrapping estimates uncertainty directly from repeated resampling rather than depending on strong assumptions about the population distribution or on an available analytical formula. This distinction matters when the statistic is complex, the sample is small, or the data are not normally distributed. It can therefore extend uncertainty analysis to situations where conventional calculations are difficult.
The resulting sampling distribution can support estimates of standard errors, confidence intervals, and bias. Standard errors describe the estimated uncertainty around a statistic, while confidence intervals express a range based on the resampling results. Examining bias helps researchers assess whether the sample-based estimate systematically differs from the values suggested by the resampled analyses.
First, begin with the observed dataset and repeatedly draw new samples with replacement. Next, calculate the selected statistic for every resample, such as a mean, median, regression coefficient, or correlation. Finally, use the collection of calculated values as a sampling distribution to estimate uncertainty, reliability, standard errors, confidence intervals, or bias.
The approach can be applied to means, medians, regression coefficients, and correlations, rather than being limited to a single type of summary. For each statistic, repeated resampling produces a corresponding set of estimates. Researchers can then evaluate the statistic’s uncertainty or reliability, including cases where a direct analytical calculation is difficult or unavailable.
Bootstrapping is particularly useful when researchers need to quantify uncertainty for complex statistics, small samples, or data that do not follow a normal distribution. Its applications span experimental, biomedical, social, and computational studies. In each setting, the method helps characterize the reliability of sample-based findings when conventional analytical formulas may be difficult to use.