Heavy‐mineral assemblages of sediments and sedimentary rocks record information regarding provenance, including the source rocks involved, tectonic setting, climatic conditions, and modifications from source to sink. Drawing conclusions on provenance and provenance changes requires robust quantification of individual heavy‐mineral species contents, including error estimates. Nevertheless, it is common practice to count sub‐quantities of grains from aliquots and not considering the bias introduced by (a) counting similar numbers of grains from aliquots containing different total numbers of grains, and (b) using variable counting methods. Consequently, reported heavy‐mineral contents estimated from counting sub‐quantities are affected by errors of unknown extent, making it infeasible to determine whether intra‐ or intersample variations are statistically significant. Based on 65 heavy‐mineral aliquots of variable grain size, mineral species contents, total number of grains, and known composition determined by counting all grains (n = 80,393), here >31 million countings of heavy‐mineral sub‐quantities are simulated using (a) ribbon counting with varying ribbon size, ribbon position, ribbon orientation, total number of counts, and ways of aggregating counts from multiple ribbons, and (b) a newly proposed counting technique called cluster counting. I show that (a) error estimation for a specific aliquot requires a finite population correction; (b) compared to adjacent ribbons, aggregating counts of spatially distant ribbons reduces the error; (c) cluster counting further reduces the error, showing the best fit with theory; and (d) the Wilson score interval enables error calculation as well as the number of grains to be counted to achieve an operator‐specific aim. Plain Language Summary: Rocks exposed to rain, ice, and wind will disintegrate, get transported, and end‐up as sediment or sedimentary rock like sand or sandstone, respectively. Such sediments are mainly composed of light mineral grains including quartz and feldspar, but additionally contain heavy minerals with a density >2.85 g/cm3. While being volumetrically subordinate, the types and proportions of different heavy‐mineral species are particularly diagnostic for the original rocks eroded to form the sediment under investigation. Commonly, heavy minerals are separated by standardized lab routines, and finally a portion of heavy minerals is counted under the microscope to estimate their content in the sediment. Unfortunately, there is no standardized routine of (a) how to count the grains, (b) how many grains to count, and (c) how to estimate the counting error. In this work, I computationally simulated countings and their errors using the most common technique (ribbon counting) with varying parameters as well as a newly proposed technique called cluster counting. I show that cluster counting not only reduces the counting error but also, more importantly, improves the predictability of the counting error. This provides scientists with a new tool to argue whether differences between different samples are statistically significant. Key Points: Counting errors and their variations are related to the capability of the applied counting technique to counteract spatial heterogeneityArea counting is inappropriate to counteract spatial heterogeneity, but spacing out ribbons evenly gives reasonable resultsA newly proposed counting technique (cluster counting) reduces the bias, enabling error calculations and the number of grains to be counted [ABSTRACT FROM AUTHOR]