There has been an enormous increase in the use of solar heating and photovoltaic systems worldwide. Building professionals to better design buildings, windows and other solar related constructs require current and accurate compilations of solar irradiance data. Frequently, enough care is not exercised with respect to quality of the measurements. Traditionally, researchers have used several methods for the quality assessment of solar irradiance data. This article addresses two of those methods, visual and quartile analysis, often used to identify "outliers" in the large datasets typically found in solar energy related research. The present work introduces another method for identification of erroneous data, based on standard deviations, using the clearness index and the diffuse ratio. This method is highly efficient in terms of its algorithmic approach.