Revealing the mechanisms governing the complex microbial community assembly is a central issue in microbial ecology. Null models are commonly used to quantitatively disentangle the relative importance of deterministic vs. stochastic processes in structuring the compositional variations. However, microbial profiling is influenced by random sampling issues, which lead to overestimated -diversity of microbial communities and may further affect stochasticity inference. By implementing simulated datasets, we investigated whether and how microbial stochasticity inference is affected by random sampling issues. Our results demonstrated solid evidences that random sampling dramatically overestimated the -diversity of microbial communities, which further led to overestimated community stochasticity inference. The effects of random sampling issues on stochasticity inference for the whole community and the abundant subcommunities were different using different null models. The stochasticity of rare subcommunities, however, was persistently overestimated no matter which null model was used. Such effects of random sampling issues on community stochasticity inference were constantly observed for communities with different -diversity. As more studies begin to focus on the different mechanisms governing abundant and rare subcommunities, we urge cautions be taken for microbial stochasticity inference based on -diversity (e.g. null models), especially for rare subcommunities with stochastic ratio slightly higher than 0.5. When necessary, the cutoff used for judging the relative importance of deterministic vs. stochastic processes shall be redefined.