ab PPV by prevalence (up to 10%) for FDA tests (E–Z). At low prevalence, such as 1%, many tests exhibit low PPV and high rates of false positives (Table 1 ). The upper limits of false positive rates for 45 (74%) tests exceed 60% and 49 (80%) tests exceed 50% given 1% prevalence. Thus, in regions with 1% prevalence, scenarios having 50% or more percent of positive serology tests corresponding to people lacking SARS-Cov-2 antibodies would be within the realm of reasonable expectations. In addition, some tests with estimated specificity below 97% have not only poor estimated false positive rates, but report high precision. Tests by Cellex, Megna, Biohit, JiangSu, and TBG are estimated to have about 80% false positives at 1% prevalence, with even the lower bounds on their FPR exceeding 66%. In such low prevalence populations, on average, anywhere between 6 and 9 out of 10 positive tests by these companies are expected to be false. Results for all tests improve with prevalence, but overall false positive rates remain concerning. Although only nine point estimates for false positive rates exceed 20% assuming 10% prevalence, which is higher than most currently estimated infection rates 30 – 32 , the upper bound for false positive rates exceed 20% for 39 out of 61 tests. Moreover, only 15 (25%) tests from six companies—Abbott (3 of 4 minus Alinity IgG, Ortho-clinical (IgG and PanIg), Siemens (all 6 varieties), BeckmanCoulter (IgG and IgM), BioMerieux (IgG) and Roche—have upper bounds on false positive rates below 10% when the prevalence is 10%. In other words, 46 out of 61 tests could reasonably produce at least 1 false positive out of 10 positive tests if the regional prevalence is 10%. Estimates or confidence bounds of exactly 100%, reported for many , are shown for completeness. These estimates should be interpreted with caution, as tests establishing sensitivity and specificity were done on small samples 33 , which may lack sufficient precision 20 , 35 for estimation wh