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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

all are fatally flawed, all have been demonstrated in the literature to be ineffective

and to lead to inconclusive results. The bottom line is that there is no way to extract

cause-effect conclusions from correlational data.

You overlooked that the causal direction might be reversed. In the case of The French

Paradox finding, I can readily see a plausible alternative interpretation as to how the

observed data could have arisen. The data do seem to show that as drinking declines

from a high to a moderate level, longevity increases. This accords with the notion that

alcohol is toxic, and that its effects are deleterious. What constitutes The French

Paradox, however, is that when one goes even farther along the drinking continuum from

moderate drinking all the way down to no drinking at all, instead of longevity

increasing still higher, the opposite happens - longevity shrinks.

What distinguishes the scientifically-trained mind from that of the layman in this case

is that the layman thinks of a single interpretation, and seizing on that as the only

one possible, stops thinking. That is, the layman thinks "Drinking not at all is

unhealthy, therefore I can improve my health by drinking." The scientifically-trained

mind, in contrast, recognizes that in correlational data a large number of

interpretations is possible, acknowledges the first interpretation that springs to mind

as one among the many that are possible, and keeps looking, and keeps finding, a number

of alternative interpretations, and ultimately acknowledges the impossibility of

choosing among them.

As illustrated in my own case. Specifically, I happen to find myself in a

naturally-constituted zero-alcohol group. That is, I drink not at all, or very close to

not at all. There is a reason for this, and that is that the effects of alcohol upon me

are toxic. Mainly, I get splitting headaches, even from the ingestion of small amounts

of alcohol, particularly if the alcohol comes in the form of wine. I take this to mean

that my constitution is weak, that I am unable to process alcohol efficiently, that I am

unable to detoxify my body of alcohol the way that others can, that my body chemistry is

not up to par. In other words, I am unwell, and as a result I do not drink.

Please mark well what I have just done - I have reversed the cause-effect conclusion

that you had come to. You concluded that not drinking causes deteriorated health, but

what I am proposing to you at the moment is that deteriorated health can cause not

drinking. The insight that I offer you is that when we observe a correlation, we don't

know what caused what, and one of the possibilities to be considered is that the causal

direction may be the opposite of our first impression, that a situation in which we

first conjectured that A causes B may prove upon more thoughtful examination to be a

situation in which B really causes A. In short, it may be the case that people who are

destined not to live as long as others tend to find themselves unable to drink alcohol.

That's all that the French Paradox may have discovered, and that's not a very good

reason for anybody to follow your recommendation to go out and start drinking.

Common sense alone invalidates The French Paradox conclusion. In other contexts, a

correlation being misinterpreted to mean that drinking promotes either health or

longevity will be obviously laughable. For example, a researcher who observes that

hospitalized patients don't drink will not conclude that teetotalling causes

hospitalization. Or, a researcher who visits death row and discovers that the inmates

don't drink and do have short life expectancies will not conclude that teetotalling

shortens life. In such examples, anyone with a modicum of common sense instantly

recognizes that a correlation between zero wine intake and either poor health or short

life does not mean that zero wine intake causes either poor health or short life. All

that is required to recognize the invalidity of your conclusion in The French Paradox is

to apply this same common sense to an only slightly more subtle case.

Are there not other studies? Undoubtedly there exist in the literature a large number of

studies that have some less direct bearing on the question that we are discussing, and

many of these studies will be genuine experiments which do permit cause effect

conclusions. I am thinking in particular of experiments that may demonstrate that

ingredients found either in grapes or in wine have a certain physiological effect. With

respect to such other studies, I make the following observations: (1) Your chief

conclusion was based not on such experiments, but on one or more correlational studies.

(2) An experiment in which subjects ingest an ingredient of grapes or of wine may

witness a certain effect, even while actually eating grapes or drinking wine produce a

different or an opposite effect. This could happen because in whole grapes or in real

wine, the ingredient with the beneficial effect could be offset by some other ingredient