to not as randomly constituted, but as naturally constituted, as if nature had come
along and assigned each subject to one of the groups. Now here comes the really
important part. It is that experience teaches us that naturally-constituted groups are
capable of differing from each other on every conceivable dimension, and are highly
likely to differ from each other substantially on a number of dimensions. In other
words, people who drink no wine are likely to differ from people who drink several
glasses of wine in many ways. Perhaps the non-drinkers will have more females, and the
drinkers will have more males - or perhaps the opposite. Perhaps the drinkers will be
older or younger. Perhaps the drinkers will be richer or poorer. Perhaps the drinkers
will tend to be single and the teetotallers tend to be married, or vice versa.
Differences may readily be discovered in height, in weight, in education. Differences
could quite plausibly be discovered in smoking, in drug use, in exposure to industrial
pollutants, in diet. People who drink will tend to live in different parts of the city
from people who don't drink. People who drink may watch more television, use microwave
ovens more, spend more time breathing automobile exhaust - or less. As people of
different ethnic backgrounds, or religions, or races drink different amounts, it follows
that people who drink different amounts will differ in ethnic background, in religion,
and in race.
One can speculate about thousands of ways in which drinkers could differ from
teetotallers, and if one actually examined two such groups, one would find a few
dimensions on which such extraneous differences were large, several dimensions on which
such extraneous differences were moderate, and a large number of dimensions on which
such extraneous differences were present but small. The hurdle that the correlational
researcher is never able to overleap is that given that he is unable to look for every
conceivable difference, he will never know all the ways in which his
naturally-constituted groups did indeed differ from each other.
Natural groups may eat different amounts of broccoli. And so then, no cause-effect
conclusion will ever be possible from a correlational study. If the moderate drinkers
happen to live longer, we will never be able to conclude that this is caused by their
moderate drinking, because it might be caused by how close they live to high-voltage
lines or how often they wash their hands or how far they drive to work or how much
toothpaste they swallow or how much they salt their food or how close they sit to their
televisions or how many pets they keep or whether they sleep with their windows open or
whether they finish their broccoli. In an experiment, random assignment of subjects to
groups guarantees equality on all such extraneous dimensions, and this makes
cause-effect conclusions possible. In a correlational study, natural assignment of
subjects to groups guarantees inequality on many such extraneous dimensions, and this
makes cause-effect conclusions impossible.
Correlation does not imply causality. Every textbook on statistics or research
methodology underlines this same caveat, captured in the expression "correlation does
not imply causality," which warns that from correlational data, it is impossible to tell
what caused what. Science has developed only a single method for determining what
caused what - and that method is the experiment. No experiment, no cause effect
conclusion - it's that simple. Given correlational data, furthermore, there is no way
of extracting cause-effect conclusions by more subtle or more advanced analyses - no way
of equating the groups statistically, no way of matching subjects to achieve
statistically the pre-treatment equality that is needed to arrive at cause-effect
conclusions. Advanced methods of analyzing correlational data do exist, and are used by
naive researchers, and to the layman may appear to be effective, but the reality is that