Decoding Intelligence Rankings: Average, Above, and Beyond

Do you ever wonder what percentage of the population can be considered above average in terms of intelligence? This question touches on a fundamental aspect of human cognition and has intrigued researchers, psychologists, and curious minds alike. In this article, we'll explore the basics of IQ scores, their distribution, and the nuances of what it means to be considered "above average."

The Nature of Average Intelligence

IQ tests are designed to measure intelligence, and one of the key aspects of these tests is how they are standardized. The mean or average IQ score is set at 100, with a standard deviation (SD) of 15. This means that, in a normally distributed population, approximately 50% of individuals will have an IQ score that falls between 90 and 109. These individuals are considered to be within the average range of IQ.

However, it's important to note that the average IQ score doesn't necessarily mean that half the population is above average and half is below average. This is because the normal distribution is symmetric. In this context, "average" means that 50% of the population scores above 100, and the other 50% scores below 100. So, by definition, a distribution that is symmetrical around the mean will always have exactly 50% of individuals scoring above the mean and 50% scoring below it.

George Carlin's Observation: An Insightful Perspective

George Carlin once made a famous observation that pokes fun at the concept of average intelligence. He remarked, "Think of how stupid the average person is. I mean really think about it. Now remember that half the people are even stupider than that!" This humorous statement touches on the paradox of average intelligence, highlighting the inverse relationship between being above average and below average.

Real-World Intelligence Distribution

Intelligence distribution can vary across different regions and populations, but generally, it follows a normal distribution. In a purely normal distribution, 50% of the population would score above the mean, and 50% would score below it. However, in the real world, this distribution can be skewed due to various socio-economic, cultural, and biological factors.

Research by Haier (2017) on the neuroscience of intelligence suggests that the distribution of intelligence can be visually represented by a bell curve. If we use the mean (IQ 100) as the reference point, the area under the curve up to and including the mean would be 50%. When considering the top 16% of the population, we're looking at scores that are two standard deviations above the mean (IQ 115).

Skewed Distribution and Beyond Average

It's worth noting that real-world intelligence distributions are often skewed to the left, meaning there is a larger proportion of individuals with lower IQ scores. This skew is due to the exclusion of individuals with organic retardation from the norming group, which is meant to represent the full population. When these individuals are included, the distribution becomes more symmetrical and closely follows a normal distribution.

However, if we consider the top 16% of the population to be above average, this means that only about 16% of the population scores in the top two standard deviations (IQ 115 and above). This means that most individuals (approximately 67%) fall within the average range, and a smaller portion (about 16%) can be considered above average.

Conclusion

Understanding the distribution of intelligence is crucial for comprehending the spectrum of human cognitive abilities. The mean IQ of 100 represents the midpoint of a normal distribution, with half of the population scoring above and the other half below. However, when considering the top 16% as the definition of being above average, it becomes clear that only a small fraction of the population fits this description.

So, while half of the population can be technically considered above average, only a specific subset of individuals (around 16%) truly falls into this category. This realization can help in better understanding the diversity of human intelligence and the complex factors that contribute to individual cognitive differences.