Floristic Quality Assessment (FQA) is increasingly influential for making land management decisions, for directing conservation policy, and for research. But, the basic ecological properties and limitations of its metrics are ill defined and not well understood–especially those related to sample methods and scale. Nested plot data from a remnant tallgrass prairie sampled annually over a 12-year period, were used to investigate FQA properties associated with species detection rates, species misidentification rates, sample year, and sample grain/area. Plot size had no apparent effect on Mean C (an area’s average Floristic Quality level), nor did species detection levels above 65% detection. Simulated species misidentifications only affected Mean C values at greater than 10% in large plots, when the replaced species were randomly drawn from the broader county-wide species pool. Finally, FQA values were stable over the 12-year study, meaning that there was no evidence that the metrics exhibit year effects. The FQA metric Mean C is demonstrated to be robust to varied sample methodologies related to sample intensity (plot size, species detection rate), as well as sample year. These results will make FQA measures even more appealing for informing land-use decisions, policy, and research for two reasons: 1) The sampling effort needed to generate accurate and consistent site assessments with FQA measures is shown to be far lower than what has previously been assumed, and 2) the stable properties and consistent performance of metrics with respect to sample methods will allow for a remarkable level of comparability of FQA values from different sites and datasets compared to other commonly used ecological metrics.