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the claim
Bootstrapping is an acceptable method to determine standard error for binding constants
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
2 sources for · 0 against

Bootstrapping is a recognized and effective resampling method used to accurately determine standard errors and confidence intervals for binding constants.

Evidence for · 2
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data.
2022 · cited by 0
Paper [0] demonstrates that bootstrapping provides accurate uncertainty quantification and asymmetric confidence intervals for binding constants derived from titration data.
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The analysis

The claim is specific, empirical, and falsifiable. The retrieved papers explicitly support the use of bootstrapping for uncertainty quantification and standard error evaluation in binding constants and protein-binding models.

More for · 1
Comparisons of Four Protein-Binding Models Characterizing the Pharmacokinetics of Unbound Phenytoin in Adult Patients Using Non-Linear Mixed-Effects Modeling.
2020 · cited by 0
Paper [1] utilizes bootstrapping as a reliable evaluation tool in the final model analysis to determine relative standard errors for protein-binding models.
The paper trail · every fact has a biography
first checked31 Jul 2026
judged → SUPPORTED · 7531 Jul 2026
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