Mineral Engineers — Statistical Methods For

Statistical Methods For Mineral Engineers: From Random Rocks to Reliable Results

Report ID: SME-STAT-2025-04
Target Audience: Plant Metallurgists, Mine Geologists, Process Engineers
Core Message: In a world of inherently variable ore, statistics is not just about averages—it’s the science of making confident decisions despite chaos.

The book provides walkthroughs and worked examples for several essential statistical tools: Experimental Design: Statistical Methods For Mineral Engineers

Spurious Correlation (High R², No Meaning): Correlating ash content in coal to the day of the week (R² = 0.85) is fun until you realize it was actually correlated with rain (humidity affecting analysis). Always validate regression with physical causality.

Ignoring the Zero: When compositing assay data, missing data (samples that were never taken or were below detection limit) cannot just be ignored. Treat "Below Detection Limit" (BDL) as 1/2 the detection limit, but recognize the bias this introduces.

• Null Hypothesis (H₀): Mean Monday recovery = Mean weekly recovery.
• Data: Monday shift (n=5 assays): 88.1, 87.6, 88.9, 87.3, 88.4 → Mean = 88.06%
  Other shifts (n=20 assays): Mean = 91.8%, σ = 1.5%

Amaya smiled. “Statistics isn’t a single number handed down from on high. It’s a conversation. We choose methods that match the rock and the questions.” Statistical Methods For Mineral Engineers: From Random Rocks