Perezgonzalez J2023-10-242022-05-032023-10-242022-05-032022http://hdl.handle.net/10179/20370In our current regime of COVID-19 testing, a question seems not to be asked: Are we inferring the best we can from our results? Or, put differently, are we testing with severity? This study thus explore the proportion of expected positives and negative cases, with an especial focus on estimating false positives in isolation and estimating false (or unknown) negatives in the remaining population. Both seems to have been chiefly ignored by Government health policy.(c) The AuthorBayesian statisticsCOVID-19Statistical inferenceSeverityinternet10.31219/osf.io/7pshw454557Massey_Dark