@article{ref1,
title="Revised decision criteria for before-and-after analyses",
journal="Transportation research record",
year="1986",
author="Weed, Richard M.",
volume="1068",
number="",
pages="8-17",
abstract="Because better experimental designs utilizing control sites are not always feasible, a simple before-and-after analysis is commonly used to analyze accident rates and other counted events. Treating the number of events counted before some experimental change as a known constant rather than as a random variable is a fundamental conceptual error that falsely inflates the confidence level at which the experimental change can be judged to have had a significant effect. For example, a reduction in the number of accidents observed after some improvement has been implemented may be judged to be statistically significant when, in fact, it is primarily the result of the chance occurrence of an unusually high &quot;before&quot; count, a typical manifestation of the &quot;regression-to-the-mean&quot; phenomenon. By properly treating the initial count as a random variable, at least a portion of this problem is avoided. New tables are developed to provide more appropriate decision criteria for applications of this type.  Record URL:  http://onlinepubs.trb.org/Onlinepubs/trr/1986/1068/1068-002.pdf<p /><p>Language: en</p>",
language="en",
issn="0361-1981",
doi="",
url="http://dx.doi.org/"
}