The abundance of health-related statistics routinely collected worldwide invites their misuse from haphazard associations between secular trends of these data. This misuse is often compounded by assessing these associations simply on the basis of a visual inspection of the data. The visual approach to data analysis, known to have several pitfalls, is particularly tempting in the context of asthma where it has often been used. For example, the epidemic of asthma deaths that occurred in New Zealand during the last two decades has been imputed to fenoterol, a medication for asthma, on the basis of a visual assessment of ecological data. The simultaneity of time trends in the asthma death rate and fenoterol market share in that country formed an important part of the statistical basis of the evidence. We verified whether the results of such visual analyses are corroborated by more objective quantitative statistical methods of analysis. We reanalyzed these same data, namely the time trend data of New Zealand asthma death rates, fenoterol market share, sales of beta-agonists and inhaled corticosteroids, measured yearly for the 16-year span 1976-1991, using Poisson weighted loglinear regression. We found that the protective effect of inhaled corticosteroids (rate ratio 0.5 per canister per month; 95% confidence interval 0.4 to 0.7; p = 0.0001) was more closely associated with changes in asthma mortality than either fenoterol (RR 2.7 per canister per month; 95% CI: 0.9 to 7.5; p = 0.06) or all beta-agonists combined (RR 1.6; 95% CI: 0.8 to 3.0; p = .19). We conclude from this quantitative analysis that these ecological asthma mortality data provide evidence of a stronger association with inhaled corticosteroids, little used in New Zealand at the onset of the epidemic but used abundantly at its termination, than with fenoterol. This conclusion is diametrically opposite to that found by the visual approach. The quantitative analysis demonstrates that the visual approach to the analysis of ecological data, although seemingly convincing, can be misleading by creating an optical illusion. This purely visual approach to data analysis may thus have serious implications when the resulting scientific information is used to make vital public health and policy decisions.