The IQ Illusion (2): Why IQ Tests Aren’t Very Clever

Let's even assume the IQ measure is sound, what does IQ claim to predict?

In Part One, we explored the challenges of measuring intelligence through IQ tests, given the complex nature of human cognition based upon the long form essay by Sean McClure. Now, let's turn our attention to the second crucial aspect of IQ testing: its claimed ability to predict future outcomes, particularly in education and job performance.

The Claim of Prediction

Proponents of IQ testing often argue that these tests can predict important life outcomes, especially educational achievement, occupational level, and job performance. But how valid are these claims when subjected to rigorous scrutiny?

The Reliance on Correlation

At the heart of IQ's predictive claims lies correlation. Researchers have found correlations between IQ scores and various measures of success. However, as we've discussed before, correlation does not imply causation. This is especially true in complex systems like human intelligence and social outcomes.

The Problem with Meta-Analyses

Much of the evidence for IQ's predictive power comes from meta-analyses of multiple studies. These analyses often use statistical corrections to increase the reported correlations. Let's examine some of the issues with these corrections:

1. Sampling Error:

Meta-analyses attempt to correct for sampling errors, assuming that individual studies are random samples from a well-defined population. However, as McClure points out:

"How likely is it that individual IQ studies come from the same population? How many employers are willing to have their employees tested, let alone supervisors willing to rate them?"

This bias means that the pooled estimates from meta-analyses may not accurately reflect any real-world population.

2. Measurement Error:

Corrections for measurement error in IQ studies often rely on incomplete or inconsistent reliability information. McClure notes:

"Widely-cited studies supporting IQ-job correlation only gathered reliability information for a subset of studies for which that information was available."

This selective use of reliability data can lead to overestimation of correlations.

3. Range Restriction:

IQ studies often correct for range restriction, assuming that their samples don't represent the full range of the population. However, these corrections are often based on questionable assumptions. As McClure states:

"Few primary studies used in IQ meta-analyses report their range restrictions."

Without accurate information on the true range of the population, these corrections can artificially inflate correlations.

The Ambiguity of Job Performance

Even if we accept the correlations at face value, there's a fundamental problem with using job performance as a criterion. Job performance is a complex, multifaceted concept that's often measured subjectively. McClure highlights this issue:

"If you asked 3 different workplace supervisors to rate the same 3 workers you would get different ideas of what constitutes 'performance.' Supervisor ratings are highly subjective, even within the same domain."

This subjectivity introduces significant noise into any attempt to correlate IQ with job performance.

The Complexity of Job Performance

Job performance, like intelligence itself, is a complex phenomenon. It involves not just cognitive abilities, but also social skills, emotional intelligence, motivation, and numerous other factors. Attempting to reduce this complexity to a simple correlation with IQ scores is a gross oversimplification.

The Self-Fulfilling Prophecy

There's also the issue of self-fulfilling prophecy when it comes to educational and occupational outcomes. If society places a high value on IQ scores, then naturally those with high scores will tend to have better educational and occupational outcomes. This doesn't necessarily mean that IQ itself is predictive; rather, it could be a reflection of societal values and structures.

The Flynn Effect

The Flynn Effect - the observed rise in IQ scores over time - poses a significant challenge to the idea that IQ tests measure some fixed, innate quality that predicts future success. As McClure notes:

"If you're surprised by the Flynn Effect, you don't understand complexity."

The fact that IQ scores have risen dramatically over the past century, far too quickly to be explained by genetic changes, suggests that what IQ tests measure is heavily influenced by environmental and cultural factors.

Conclusion

While IQ tests may show some correlation with certain life outcomes, the claims of their predictive power are often overstated and based on flawed statistical analyses. The complexity of human intelligence, job performance, and life success defies simple, linear predictions based on a single test score.

As we continue to study human intelligence and performance, we need to embrace approaches that can handle this complexity. This might involve:

1. Developing more comprehensive models that incorporate multiple factors beyond cognitive ability.

2. Using more sophisticated statistical techniques that can handle nonlinear relationships and interactions between variables.

3. Recognising the limitations of our predictive models, especially when dealing with complex human behaviors and outcomes.

Ultimately, while IQ tests may provide some useful information, they should not be treated as crystal balls that can predict a person's future success or potential. Human potential is far too complex and multifaceted to be captured by a single number or even a set of test scores.

As we move forward, it's crucial that we develop a more nuanced and comprehensive understanding of human intelligence and its relationship to real-world outcomes. Only then can we hope to create truly effective and fair systems for education, employment, and personal development.