{ "background": "Power distribution losses in sub-Saharan Africa remain persistently high, undermining grid reliability and economic development. Technical losses, stemming from inefficient network equipment, are a critical yet under-measured component. There is a paucity of robust field data comparing the real-world performance of different equipment systems under operational conditions.", "purpose and objectives": "This case study presents a methodological evaluation of a randomised field trial designed to quantify efficiency gains from alternative distribution equipment. The primary objective was to establish a rigorous field-testing protocol and apply it to compare the performance of conventional conductors against modern low-loss alternatives in a real network.", "methodology": "A randomised controlled trial was implemented across multiple rural feeders. Treatment and control groups were assigned using stratified randomisation based on feeder length and load. Performance was measured using high-resolution power quality analysers installed at distribution transformers. The core analysis employed a differences-in-differences model: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y_{it}$ is technical loss. Robust standard errors were clustered at the feeder level.", "findings": "The methodological approach proved viable for isolating equipment-specific effects. The intervention group using modern conductors demonstrated a statistically significant reduction in technical losses of 2.8 percentage points (95% CI: 1.7 to 3.9) compared to the control, after controlling for baseline load and environmental factors.", "conclusion": "The randomised trial methodology provides a robust framework for evaluating distribution equipment efficiency in field settings. The results confirm that equipment choice is a significant determinant of network losses.", "recommendations": "Utilities should adopt randomised field-testing protocols for major equipment procurement decisions. Regulators should consider incorporating such real-world efficiency data into loss-reduction targets and investment approvals.", "key words":