Annual review of public health
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Annu Rev Public Health · Jan 2002
ReviewThe importance of the normality assumption in large public health data sets.
It is widely but incorrectly believed that the t-test and linear regression are valid only for Normally distributed outcomes. The t-test and linear regression compare the mean of an outcome variable for different subjects. ⋯ We discuss situations in which in other methods such as the Wilcoxon rank sum test and ordinal logistic regression (proportional odds model) have been recommended, and conclude that the t-test and linear regression often provide a convenient and practical alternative. The major limitation on the t-test and linear regression for inference about associations is not a distributional one, but whether detecting and estimating a difference in the mean of the outcome answers the scientific question at hand.
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Reductions in motor vehicle injury and death represent a major public health success. Since the advent of the federal program in highway safety in 1966, motor vehicle deaths have dropped dramatically, not only in rates per miles driven but also in absolute numbers. ⋯ Although progress has been made on many fronts, major areas addressed here include federal motor vehicle safety standards, alcohol safety programs, occupant restraint laws and usage, and speed limits. The achievements in motor vehicle safety provide a model for other injury control efforts.