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However, despite its simplicity, bootstrapping can be applied to complex sampling designs (e.g. for population divided into s strata with n_s observations per strata, one example of which is a dose-response experiment, where bootstrapping can be applied for each stratum). Bootstrap is also an appropriate way to control and check the stability of the results. Although for most problems it is impossible to know the true confidence interval, bootstrap is asymptotically more accurate than the standard intervals obtained using sample variance

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