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Hosseinzadeh et al. (2016) identified the importance of a balance of efficiency gains and losses for 33 Australian mining firms by using a bootstrap approach.
To account for the difference in sample size between small and large populations, we tested the robustness of the results of our analyses by using a bootstrap procedure [37].
To account for the asymmetry in sample size between small and large populations, we tested the robustness of this F-test by using a bootstrap procedure [37] and found results that were consistent with the original test (complex environment: P = 0.043; simple environment P = 0.45).
For this purpose, we analyzed whether the increase in the population spike rate in the first or second bin after thalamic stimulation was significantly greater than could be due to chance fluctuations in the baseline rate of the population by using a bootstrap randomization to reshuffle stimulation times.
Alternatively one could compute confidence intervals by using a bootstrap approach [ 11].
We compared the AUCs by using a bootstrap method (2,000 samples) [ 16].
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Velocities were calculated by using a bootstrapping procedure (mean±s.d.; N=1,000 bootstrap estimates from re-sampling with replacement).
(c) Velocities from a c without (black) and with (red) synaptic antagonists calculated by using a bootstrapping procedure (mean±s.d.; N=1000 bootstrap estimates from re-sampling with replacement; dots above the plot indicate significant differences, P-value <10−3, Mann–Whitney U-test).
Enrichment was calculated by using a bootstrapping approach (see Materials and Methods).
A classification was proposed based on the estimated relatedness developed by using a bootstrapped maximum-likelihood unrooted tree generated by PHYML (1,000 rounds of bootstrapping) and drawn using FigTree 1.3.1.
Having established the reliability, convergent validity, and discriminant validity of all the constructs in the model, the next step is to test the structural model for the hypothesized paths by using a PLS bootstrap sampling method to estimate the path coefficients.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com