How many replicate tests are needed to test cookstove performance and emissions? — Three is not always adequate

Abstract

Almost half of the world's population still cooks on biomass cookstoves of poor efficiency and primitive design, such as three stone fires (TSF). Emissions from biomass cookstoves contribute to adverse health effects and climate change. A number of improved cookstoves with higher energy efficiency and lower emissions have been designed and promoted across the world. During the design development, and for the selection of a stove for dissemination, the stove performance and emissions are commonly evaluated, communicated and compared using the arithmetic average of replicate tests made using a standardized laboratory-based test, commonly the water boiling test (WBT). However, the statistics section of the test protocol contains some debatable concepts and in certain cases, easily misinterpreted recommendations. Also, there is no agreement in the literature on how many replicate tests should be performed to ensure “confidence” in the reported average performance (with three being the most common number of replicates). This matter has not received sufficient attention in the rapidly growing literature on stoves, and yet is crucial for estimating and communicating the performance of a stove, and for comparing the performance between stoves. We illustrate an application using data from a number of replicate tests of performance and emission of the Berkeley–Darfur Stove (BDS) and the TSF under well-controlled laboratory conditions. Here we focus on two as illustrative: time-to-boil and emissions of PM2.5 (particulate matter less than or equal to 2.5μm in diameter). We demonstrate that an interpretation of the results comparing these stoves could be misleading if only a small number of replicates had been conducted. We then describe a practical approach, useful to both stove testers and designers, to assess the number of replicates needed to obtain useful data from previously untested stoves with unknown variability.