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Version vom 19. Juni 2008, 14:47 Uhr
Safety in numbers is the hypothesis that, by being part of a large physical group or mass, an individual is proportionally less likely to be the victim of a mishap, accident, or other bad event.
Description
Evidence often advanced for this position includes the flocking of birds and shoaling of fish. In both of these instances, by being part of a large group, individuals face less risk of falling victim to predators than they would if traveling alone.
In road traffic safety
Safety in numbers is also used to describe the theory that a particular road user, especially a pedestrian or cyclist, becomes less likely to be involved in a crash, as the population of similar road users increases. A Public Health Consultant has concluded that the theory is correct, based on statistical analysis of collision data.[1] A Cycling Transportation Engineer has disputed that conclusion, writing that the data used is insufficient to demonstrate that there is a cause-and-effect relationship.[2]
Case studies
After cycling was promoted in Finland, the number of trips increased by 72% and there was a 75% drop in cyclists deaths.Vorlage:Fact[3]
In the United Kingdom, Transport for London, the body responsible for the London Congestion Charge, stated in their April 2005 review of that scheme, that motor vehicle traffic had decreased by 16%, bicycle use had increased by 28% and cyclist injuries had decreased by 20% in the first year of operation of the scheme.[4] In January 2008, a London newspaper reported that the number of cyclists in London, being treated in hospitals for serious injuries, had increased by 100% in six years. Over the same time, they report, the number of cyclists had increased by 84%.[5]
While such data shows a degree of correlation, conclusions of causality may very well be based on a statistically spurious relationship. In other words, the relationship of an increase in bicycle use and a decrease in cyclist injuries may have no actual causal connection due to a certain third, unseen factor (referred to in statistics as a "confounding factor" or "lurking variable"). A spurious relationship gives an impression of a worthy link between two groups that is invalid when objectively examined.[6]
See also
- Critical Mass
- Predator satiation
References
Vorlage:Ecology-stub Vorlage:Psych-stub Vorlage:Statistics-stub Vorlage:Cycling-stub