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Parametric and nonparametric coupled with bootstrap simulation technique were used to reevaluate previouslydefined reference intervals of serum chemistry parameters. A population-based study was performed in 100 clinicallyhealthy dogs that were retrieved from the medical records of Kangwon National University Animal Hospital during2005-2006. Data were from 52 males and 48 females (1 to 8 years old, 2.2-5.8 kg of body weight). Chemistry parametersexamined were blood urea nitrogen (BUN) (mg/dl), cholesterol (mg/dl), calcium (mg/dl), aspartate aminotransferase(AST) (U/L), alanine aminotransferase (ALT) (U/L), alkaline phosphatase (ALP) (U/L), and total protein (g/dl), andwere measured by Ektachem DT 60 analyzer (Johnson & Johnson). All but calcium were highly skewed distributions.parameters, ranging 5-9% of the samples and the remainingwere only 1-2%. Regardless of distribution type of each analyte, nonparametric methods showed better estimates foruse in clinical chemistry compare to parametric methods. The mean and reference intervals estimated by nonparametricbootstrap methods of BUN, cholesterol, calcium, AST, ALT, ALP, and total protein were 14.7 (7.0-24.2), 227.3 (120.7-480.8), 10.9 (8.1-12.5), 25.4 (11.8-66.6), 25.5 (11.7-68.9), 87.7 (31.1-240.8), and 6.8 (5.6-8.2), respectively. This studyindicates that bootstrap methods could be a useful statistical method to establish population-based reference intervalsIn addition, the results emphasize on the confidence intervals of the analytical parameters showing distribution-relatedvariations.