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A B S T R A C T Predicting the size of the largest defect expected to occur in components based on samples obtained from polished inspection areas is a common exercise, which is even addressed in standards. However, the standard practice may occasionally yield poor results. This paper presents a comprehensive method that aims to improve some of the shortcomings of the standard practice. The method is utilized on actual defect data, which showed that the proposed method is able to predict significant experimental observations that the standard practice missed.Keywords statistical model; statistics of extremes; steel; steel cleanness. N O M E N C L A T U R EA 0 = inspection area a = distribution parameter b = distribution parameter F X = Probability distribution of X (dummy variable) k = number of inspection areas S A = observable defect size (small circle) S V = representative defect size (great circle) V 0 = inspection volume V = target volume Z A = largest observable defect size Z V = largest representative defect size α = probability level γ = distribution parameter η = distribution parameter μ = distribution parameter ρ = distribution parameter (defect density)

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