Absctract.This article presents a comparative analysis of uncertainty evaluation methods, GUM (Guide to the Expression of Uncertainty in Measurement) and Monte Carlo, using the calibration of a bimetallic thermometer TBP 63-1 with a measurement range of 0 to 120 °C and an accuracy class of 2.5. The study focuses on the identification and quantitative analysis of key uncertainty factors, including both stochastic and systematic error components. GUM modeling requires a high level of mathematical expertise to perform many procedures, while the Monte Carlo method serves as an alternative for various laboratory studies. A mathematical model is introduced to describe the measured temperature, represented as  , where each component reflects its respective contribution to overall uncertainty:  is the thermometer reading,  is the calibrator correction, and  is half the scale division. The analysis process includes calculating the type A standard uncertainty from a series of measurements to assess random fluctuations, while type B standard uncertainties are estimated for systematic sources, assuming uniform and rectangular error distributions. The combined standard uncertainty is integrated to obtain the expanded uncertainty with a coverage factor k=2 and a confidence level of 0.95. Using the Monte Carlo method, 1,000,000 simulations were generated to ensure statistical significance. The analysis results are presented as the final corrected temperature value, accounting for all calculated uncertainties. The primary goal of the study is to determine which method is more effective for this type of measuring equipment and to demonstrate the simplicity of using Microsoft Excel (or similar spreadsheet software) for evaluating measurement uncertainties based on functional dependence.

Keywords: Monte-Carlo method, GUM, bimetallic thermometer, uncertainty, error, Microsoft Excel.