Fear of Covid-19 is based on its high estimated case fatality rate-2% to 4% ... according to the World Health Organization and others. We believe that estimate is deeply flawed. There's little evidence to confirm that premise-and projections of the death toll could plausibly be orders of magnitude too high. The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases. The latter rate is misleading because of selection bias in testing. The degree of bias ... could make the difference between an epidemic that kills 20,000 and one that kills two million [in the U.S.]. First, the test used to identify cases doesn't catch people who were infected and recovered. Second, testing rates were woefully low for a long time and typically reserved for the severely ill. Together, these facts imply that the confirmed cases are likely orders of magnitude less than the true number of infections. Epidemiological modelers haven't adequately adapted their estimates to account for these factors. This does not make Covid-19 a nonissue. The daily reports from Italy and across the U.S. show real struggles and overwhelmed health systems. But a 20,000- or 40,000-death epidemic is a far less severe problem than one that kills two million. Given the enormous consequences of decisions around Covid-19 response, getting clear data to guide decisions now is critical. We don't know the true infection rate in the U.S. If we're right about the limited scale of the epidemic, then measures focused on older populations and hospitals are sensible. A universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health.