Likelihood of confusion surveys routinely employ Test/Control (T/C) designs—a scientifically sanctioned approach for detecting the presence of likelihood of confusion and for assessing its magnitude. Based upon the results presented by survey research experts, judges and juries are asked to determine if results support a finding for the plaintiff or not.
In other words, judicial decision-makers are charged with: (1) determining the presence of alleged infringements, and (2) evaluating the significance of the findings in forming a decision. In many cases, that determination is made on the basis of either case-by-case subjective evaluation or precedent. Of course, what we call precedent today was a subjective, though well-reasoned, assessment at the time of its establishment.
The purpose of this paper is to argue for giving the science of statistics a chance to determine significance in likelihood of confusion cases as it does in many other fields, ranging from F.D.A.-required new drug approvals to basic science experiments in fields as diverse as biology, behavioral economics, experimental psychology, or physics and chemistry.
Why Use Test/Control Designs?
Before getting into the heart of the argument let us review the reasons for using T/C designs in likelihood of confusion cases. The process of detecting likelihood of confusion benefits greatly from the T/C design by allowing us to compare the results obtained from measuring the allegedly infringing mark, word, design, etc., to the effect of a stimulus that would not have given the plaintiff reason to launch a complaint in the first place, ceteris paribus.
The Control stimulus is the equivalent of a placebo in new drug research—it “controls for” (by keeping them constant across both the Test and the Control cells) all the variables that might have affected the results produced by the test stimulus except the critical factor that prompted the lawsuit.
While adding a control cell heaps additional data collection costs on the survey, it has the beneficial effect of eliminating all other possible explanations for the test results turning out the way they did except the reason that set off the suit originally. This procedure brings us as close as possible to a cause-effect relationship as can be created in survey research.
What Gets Measured in Test/Control Designs?
The T/C design yields two results in both the Test and the Control cell:
- The proportion of consumers who think that the plaintiff’s and defendant’s products are made by the same company or by companies that are affiliated, connected, associated, or related to one another by means of licensing or permission.
- The proportion of consumers who do not think that any of the above relationships exists.
When the proportion of respondents that think there is a relationship between the two products to which they have been exposed in the Control cell is subtracted from the same proportion in the Test cell, the result is a net likelihood of confusion value. If that value is greater than zero, we conclude that likelihood of confusion is present. The next decision that needs to be made at that point is how significant is the relationship in the eyes of the law. Does the likelihood of confusion value found by the expert justify finding for the plaintiff?
In summary, T/C designs cover two areas: presence and significance of likelihood of confusion.
What Are the Guiding Principles for Determining Significance?
There are two ways for determining significance:
The first method is employed when the determination is totally judgmental. The decision-maker will be pursuing a maximal decision rule such that the larger the net value of the likelihood of confusion she has found, the higher the confidence that the results are not simply a research artifact and that they must represent what is really going on in the marketplace.
||Maximal net likelihood of confusion
||The larger, the better
||Statistical significance of net likelihood of confusion
||Just enough to eliminate pure chance as the explanatory reason
The second method uses the traditional statistical test of difference between two proportions to determine if the results are statistically significant at 95 percent confidence, the level traditionally used in survey research.
The major difference between the two methods—in addition to the first being subjective and the second being scientific and completely objective—is in the evidence required by them. When using the subjective method, the decision maker looks for large differences between Test and Control; the higher the net likelihood of confusion values the higher his or her confidence in finding for the plaintiff. By contrast, statistical decision-making is driven by finding just enough of a difference between Test and Control to determine whether or not the difference is significant or could it have happened by chance.
“The more the better” rule has strong intuitive appeal, but intuition is not always a good estimator of what is going on in the real world. The statistical significance method is superior. By eliminating the possibility of chance it is a far more precise and a far sharper measuring device because it can “spot” likelihood of confusion as soon as it happens vs. subjective estimation, which relies solely on intuition to detect when likelihood of confusion has actually happened.
The subjective method errs most often on the side of the defendant as, for example, when jurors decide that, say, 20 percent net likelihood of confusion is not enough when, unknown to them, 11 percent might have been enough to satisfy the objective criteria used by statistical testing. Statistical significance does not have the propensity to err in any direction.
Finally, if there is a choice between a precise, science-based, measurement and an intuitive measurement, wouldn’t justice be better served by relying on objective criteria that eliminate all uncertainties?