NONE 20260610115611913 wseas/wseas content-registration-form+ja@crossref.org content-registration-form International Journal of Computational and Applied Mathematics & Computer Science 2769-2477 M. Maswadah Department of Mathematics, Faculty of Science Aswan University Aswan, EGYPT It is well known that conditional confidence intervals for unknown distribution parameters are often as efficient as Bayesian confidence intervals based on non-informative priors. Accordingly, the main objective of this work is to derive conditional point estimates for the unknown parameters of the Weibull distribution using pivotal functions, and to compare them with Bayesian point estimates through Monte Carlo simulations. The simulation results indicate that, under the generalized progressive hybrid-censoring scheme, the conditional point estimates are highly efficient and outperform their Bayesian counterparts. 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