Haven't read it completely or paid attention to the data yet, but the conclusion got me hooked:
> Conclusion
> The foregoing review of the statistical case for helmet legislation suggests that the faith of the BMJ in the reliability of the statistical evidence on the effect of helmet legislation is misplaced. But debates about sophisticated statistics should not be allowed to drown out the voice of common sense. The risk compensation hypothesis is essentially common sense. There is a wealth of evidence from everyday experience that suggests that people's behaviour is influenced by their perception of risk. People tend to be more cautious when up high ladders than when up low ladders. They tend to take more care when standing on the edge of a high precipice than when standing on a low kerb. They tend to slow down when they encounter bends in the road or patches of fog and to speed up when the road becomes straight or the visibility good-and so on. The possible illustrations of the phenomenon are countless.
> There is an impressive amount of propaganda designed to encourage people to believe that they are very much less vulnerable when using seat belts and motorcycle helmets, and common sense suggests that driving behaviour will be influenced by safety devices that diminish the user's sense of vulnerability. What unaided common sense cannot predict is whether the behavioural changes induced by a safety device will partly, completely, or more than completely nullify the intended effect of the device.
> The statistics which the NHTSA considers the most compelling evidence in support of helmet legislation are displayed in figure 1. The state which Watson et al (1980) consider has had the greatest increase in deaths as result of helmet law repeal is Utah.
> When the distorting effects of the analytical methods employed by the NHTSA and Watson et al are removed, their data (with the caveats discussed above) suggest that the effect, if any, of helmet legislation on motorcycling fatalities is perverse.
I'll have to read the paper with more attention later to see if I agree with this, but it's a fascinating hypothesis regardless.
Also, this is tangential, but I'm always impressed how different conclusions can be based on the same sets of data. Statistics is a truly fascinating field of mathematics because it's still an objective topic (regarding numbers, in isolation) but becomes very human and subjective when it comes to the different conclusions, transformations, and visualizations for the same data sets.