How can you improve your sampling and QA/QC protocols to reduce bias?

We use this expression a lot in the industry, "purpose built". Additional complexity will introduce the potential of added bias. It is often highly affective at controling or limiting bias through imitating scopes to practicality and the right degree of granulation. Just because programs were done a certain way, does not necessarly mean they shouldn't be adjusted or done differently.

Objectives includes ensuring accurate resource estimation, minimizing sampling bias, and maintaining data integrity. Clear objectives help guide the development of effective protocols and ensure that sampling and QA/QC processes align with the overarching goals of reliable data collection and analysis in the mining industry.

To reduce bias in sampling and QA/QC protocols, implement random and blind sampling, ensuring each sample is equally likely and collectors are unaware of expectations. Standardize procedures, regularly calibrate equipment, and include duplicate samples for reproducibility assessment. Introduce certified reference materials to identify and correct biases. Cross-validate results from different methods or labs and provide thorough training to personnel. Maintain detailed documentation for transparency and traceability. Integrate quality control checks and conduct independent reviews to detect biases. Foster a culture of continuous improvement, regularly updating protocols based on experience and advancements.

Select appropriate sampling methods in mining by considering the deposit's characteristics, goals of sampling, and available resources. Common methods include random, systematic, and stratified sampling. Tailor the approach based on ore variability, geology, and project scale. Regularly reassess and adapt methods to account for changes in the mining operation or exploration phase. Consult industry guidelines and collaborate with experienced geologists to choose methods that align with specific project requirements.

Implement quality control in mining by establishing clear SOPs, using CRMs, incorporating blind duplicates, regularly calibrating equipment, conducting IQC and EQA, validating data, training staff, maintaining audit trails, and embracing continuous improvement.