Statistical techniques are essential in precision metrology for ensuring accurate measurements and reliable data analysis across various industries. This review explores the applications and best practices of statistical methods in precision metrology, emphasizing their role in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. Statistical techniques are used to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. Key applications include experimental design, statistical process control (SPC), uncertainty evaluation, and regression analysis. Experimental design methods such as Design of Experiments (DOE) help optimize measurement processes by identifying influential factors and their interactions. SPC techniques like control charts are used to monitor and maintain measurement processes within specified limits, ensuring consistency and reliability. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty. Best practices in statistical techniques include proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and careful interpretation of results. The review also highlights real-world case studies and practical examples from industries such as automotive, semiconductor manufacturing, aerospace, and pharmaceuticals, demonstrating the effectiveness of statistical methods in solving metrology challenges and optimizing processes. Future directions in statistical metrology include the integration of artificial intelligence and machine learning, enhanced metrology for additive manufacturing, and advanced uncertainty quantification methods. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.Statistical techniques are essential in precision metrology for ensuring accurate measurements and reliable data analysis across various industries. This review explores the applications and best practices of statistical methods in precision metrology, emphasizing their role in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. Statistical techniques are used to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. Key applications include experimental design, statistical process control (SPC), uncertainty evaluation, and regression analysis. Experimental design methods such as Design of Experiments (DOE) help optimize measurement processes by identifying influential factors and their interactions. SPC techniques like control charts are used to monitor and maintain measurement processes within specified limits, ensuring consistency and reliability. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty. Best practices in statistical techniques include proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and careful interpretation of results. The review also highlights real-world case studies and practical examples from industries such as automotive, semiconductor manufacturing, aerospace, and pharmaceuticals, demonstrating the effectiveness of statistical methods in solving metrology challenges and optimizing processes. Future directions in statistical metrology include the integration of artificial intelligence and machine learning, enhanced metrology for additive manufacturing, and advanced uncertainty quantification methods. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.