Note the "wiggle" in the vertical axis to indicate to the viewer that it does not return to zero. Although the vertical scale might be regarded as exaggerating the effect of the crackdown (traffic fatalities drop by 12%), it is probably not seriously misleading.

The real problem with this graph is that only two points are plotted. The viewer of the graph, who knows about the 1956 crackdown on speeding, is liable to attribute the sudden drop in traffic fatalities to the crackdown. However, this impression is misleading, as the following graph shows:

This graph displays traffic fatalities from 1951 to 1959,
including the drop between 1955 and 1956. On this
presentation, the 1955-56 drop is far less striking. On the
one hand, after the crackdown the number of traffic fatalities
continues to decrease from 1956 to 1959, so perhaps the
crackdown has had a prolonged effect. On the other hand,
there are equally large drops from 1951 to 1952 and from 1953
to 1954, before the crackdown, which suggests that the drop
after 1955 could be just due to the natural variability in the
number of fatalities from year to year. What's more, the
crackdown comes after (and possibly because of) a particularly
high number of fatalities in 1955. After such a peak, the
number of fatalities is likely to fall even without any
intervention; this is the phenomenon of *regression to the
mean* mentioned previously.

The source of the data is Donald T. Campbell, "Measuring the
Effects of Social Innovations by Means of Time Series" in
Judith M. Tanur et al., eds. (1989), *Statistics: A Guide
to the Unknown*. Pacific Grove, CA: Wadsworth. This article
contains an extended discussion of this example and related
problems.