Another Heinous “Scientific” Study

Study: Blue-Eyed People ‘Smarter’ Than Brown-Eyed

In this “study” we read:

Light-eyed individuals and even light-eyed animals perform better at behaviors requiring delay, self-pacing, or non-reactors, while dark-eyed individuals and animals perform better at behaviors requiring speed, sensitivity or reactivity, according to a paper authored by Kentucky’s University of Louisville professor emeritus Joanne Rowe and e-mailed to Foxnews.com.

Self-paced activities have been defined as those requiring a response made at a time chosen by the respondent when the situation remains relatively static such as bowling, golf, pitching a baseball, or tossing a ball at a target. Reactive activities are those requiring a quick response to a rapidly changing stimulus such as boxing, defensive football position, hitting a baseball, or rotary pursuit.
One study found, for example, that in professional sports light-eyed pitchers performed better than dark-eyed, but for hitters the reverse was true.

Another study found that professional light-eyed white basketball players had higher percentages of free throws, while dark-eyed white players had higher percentages of field goals.

This, however, is complete bunk. Ironically, since I’ve been doing research on Darwinism for my latest project, I happen to have at hand the reason why the above is bunk too. David M. Raup wrote about it in his book Extinction (1991. New York: W. W. Norton Company, Inc.), although he was specifically dealing with the reasons why certain animals survive extinction episodes. In the book, he writes:

Size is not the only trait that suggests a proneness to extinction. It is commonly held, for example, that tropical organisms are more likely to go extinct than their relatives in cooler climates. Planktonic organisms are said to be at greater risk than bottom-dwelling aquatics, and marine reef communities more vulnerable than nonreef communities.

My own feeling is that most of these claims are not worth a [darn]! Sadly, to test such claims is nearly impossible. Let me explain. Suppose we are studying one particular extinction event and have a list of victims and survivors. Such lists tend to be rather short, especially if we are working at a high taxonomic level (order, class, family). …Small numbers make statistical testing tricky.

Once we have the lists, we must search for common denominators: characteristics shared by most victims but not survivors, or vice versa. This is straightforward, and we have seen the results in the case of mammalian body size. The problem is that organisms have a virtually unlimited number of characteristics that might be important: anatomical, behavioral, physiological, geographical, ecological, and even genealogical. We can compare lists of victims and survivors with so many different traits as we have energy. If the lists are not long, it becomes virtually inevitable that we will find one or more traits that match the lists closely enough for us to make a case.

If we find an interesting correlation by this procedure, we can apply standard statistical tests to evaluate the possibility that the correlation is due to chance alone. Each such test asks, in one way or another, “What is the probability that the random sprinkling of a particular trait among species would, by chance, yield a correlation as good as the one we observe?” If that probability turns out to be very low—say, 5 percent or less—we feel comfortable in rejecting random sprinkling and concluding that the observed correlation is true cause and effect.

The fatal flaw in this logic is that testing cannot be adjusted for the fact that we tried many traits before finding a promising one. Remember that one out of every twenty completely random sprinklings will, on average, pass our test if odds of twenty to one are considered acceptable—as is common in scientific research. Because it is virtually impossible to keep track of the number of traits we have considered—many were discarded at a glance—we cannot evaluate the test results for any one trait (p. 96-97).

In other words, when we evaluate whether something is correlated to a specific trait, we have to account for the fact that certain traits appear to be causal even when they are not; these traits form the error-bar for the experiments involved. That is, if we assume (as Raup does above) that there is a one in twenty chance of any specific trait being accidentally correlated to look like a cause-and-effect relationship, then if we examine more than twenty traits, the odds are that one of them is going to show correlation despite there not being any actual correlation involved. Further, since there are thousands of traits to pull from, without our being able to test all of them, we are unable to say whether our correlation between a trait and an effect really is cause-and-effect, or if it is just a statistical fluke that happens.

While this may not seem obvious at first, Raup provides some examples that settle the issue one and for all:

This problem is not unique to paleontology, or to science either. If you have difficulty accepting my reasoning, try some experiments yourself. Take some baseball statistics or election results or anything that will provide a list of winners and losers. Fifty or a hundred results should be adequate. Then inspect the list to see what characteristics the winners or the losers have in common. The pattern does not have to be perfectly consistent—a statistical tendency is enough—and you are free to change the ground rules as you go along. You can even redefine winner and loser if this will help. Pay special attention to the smaller category of outcomes. For example, you may wish to compare characteristics of first-place baseball teams with those of all other teams. The shorter list (first-place teams) is more likely to have things in common than the longer list. If so, you may be able to venture conclusions like “Most managers (or all, if you are lucky) of first-place teams are firstborns, whereas managers of other teams follow the national average” (p. 97-98)

Indeed, when looked at in this manner, it is obvious why this happens. To provide another example of my own, take one person you know. Write out the traits that you and this other person have in common. The odds are that there is at least one trait that you have in common with each other that is not a statistically average trait. Say, for instance, that you both wear glasses and have short hair. If you happen to team up in a game of two-on-two basketball and win, you can claim that your edge is due to the fact that you both wear glasses and have short hair, when in reality there was no correlation at all there. Conversely, if you lose the game, you can say that you lost because you both wear glasses and have short hair.

To show how ridiculous this is, Raup gave a tongue-in-cheek example using the World Wide Atlas from Readers Digest’s 1984 edition to demonstrate that the most populous cities begin with letters in the last half of the alphabet, therefore people tend to flock towards cities that have this attribute. The data is simple. The seven most populous cities (in 1984) were: Tokyo-Yokohama, New York City, Mexico City, Osaka-Kobe-Kyoto, Sao Paulo, Seoul, and Moscow. All of them start with letters in the M-Z range of the alphabet. The next seven cities, however, were: Calcutta, Buenos Aires, London, Bombay, Los Angeles, Cairo, and Rio de Janeiro. Of these, only Rio de Janeiro does not fit the pattern. Thus, Raup states (again, tongue-in-cheek): “The statistical likelihood that this was caused by chance alone is so small that rejection of a hypothesis of randomness is routine. Cause and effect is clearly indicated (p. 99).”

So let us return to the study that tells us that blue-eyed people are smarter than brown-eyed people. Here we have a short list (smart people—however that is defined) to compare with a big list (everyone else). It is not at all improbable to find some attribute in the short list that is at a higher rate than in the longer list. Blue eyes happens to be one of these traits. Now, the question is, how do we determine whether this is a random link (just as the random cities listed above) or that it is actually cause-and-effect?

The short answer is: we can’t tell.

And therefore, this study isn’t worth the paper it was written on.