Despite all the evident benefits of miniaturized particulate matter (PM) sensors, there is an inherent drawback in the uncertainty and validity of the measurement, which is closely related to the discrete nature of particulates suspended in air. The miniaturization of those devices not only leads to smaller footprints of the devices themselves but also to smaller samples of air being measured. Even if a perfect measurement system is assumed, there is an uncertainty in assigning a particle concentration value representative for the environment due to the inherent variability of the particulate matter concentration on small scales. This stems from the fact that particles are stochastically distributed in the air leading to a non-uniform concentration for arbitrarily small volumes. Consequently, there is an uncertainty according to counting statistics, as the number of investigated particles in small samples of air is also low. Depending on the metric, additional contributions to the uncertainty occur. This is reasonable since a small number of particles cannot ideally capture the distribution of particle sizes especially since the size distribution extends over orders of magnitudes. This distribution related uncertainty adds to the uncertainty resulting from counting statistics for surface and mass related metrics. We find an influence stemming from the distribution of particle mass density, which adds to the uncertainty for mass metrics such as PM1, PM2.5 or PM10. We investigated the expected measurement uncertainty by analytical means leading to the conclusion that the distribution of particle sizes, the sample size and the ambient particle concentration have a major impact on the measurement uncertainty for the range of conditions considered. We find the distribution of particle mass density to have a minor impact on the uncertainty of mass related metrics. This uncertainty has not been discussed in the current literature to the best of our knowledge.