When ore characteristics such as mineral grain size distributions are quantified using measurements on particulate samples there is an error associated with the measured values. The magnitude of this error is a function of the grade of mineral of interest, the texture of the ore and the number of ore particles measured in the analysis. In practice the desire to minimise the error due to sampling by increasing the number of particles measured must be balanced against the increase in time and cost of analysing this increased number of particles. A statistical method based on bootstrap resampling has been developed to estimate the error in measurements of textural characteristics which are quantified by automated mineralogy systems. An application of the method to estimate the error in measurements of mineral grain size distribution is presented; however, the method can equally be applied to estimate the error in other textural characteristics, for example mineral association. By estimating how the error in the characteristic of interest reduces as particle sample size increases, the bootstrap resampling approach assists mineralogists to identify how many particles must be analysed to achieve the desired variance in the measured value. Examples from a copper porphyry ore are presented to illustrate the practical applications of this methodology in quantitative mineralogy programmes.