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The AU nonparametric bootstrap test was used to compare alternative phylogenetic hypotheses.
A bootstrap test was used to determine the percentage of replicate trees in which the associated taxa clustered together [ 45].
Once validated, a bootstrap test was used to examine the significance between the clustering genes and the phenotypes.
In addition, a parametric bootstrap test was used to estimate interaction P-values adjusted for multiple testing [ 15].
The method of neighbor-joining with 1,000 replications in the bootstrap test was used for phylogenetic analysis (7 ), and bootstrap values >50% were considered significant.
Given the non-normal estimator of interest (a difference of correlation coefficients), a bootstrap test was used to compare predictions of the different models full subunit, single-cone subunit, and LN.
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The standard error estimates of the voxel weights/saliences in each singular image from the bootstrap tests are used to assess the reliability of the nonzero saliences in significant LVs.
In the case of violation of the assumptions (e.g., on-normality), a bootstrap-type test was used.
Bootstrap hypothesis testing was used to compare differences between mean DOCAs.
The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons.
The bootstrap estimation and modified Jacknife test were used to derive 95% confidence intervals for differences in the responsiveness estimates.
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bootstrap simulation was used
bootstrap estimation was used
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bootstrap validation was used
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