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IQ
What is an IQ Score?
Originally, IQ, or Intelligence Quotient, was used to detect persons of lower intelligence, and to detect children of lower intelligence in order to place them in special education programs. The first IQ tests were designed to compare a child's intelligence to what his or her intelligence "should be" as compared to the child's age. If the child was significantly "smarter" than a "normal" child of his or her age, the child was given a higher score, and if the child scored lower than expected for a child of his or her age, the child was given a lower IQ score.
Today IQ testing is used not primarily for children, but for adults. Today we attempt to write tests that will determine an adult's true mental potential, unbiased by culture, and compare scores to the scores of other adults who have taken the same test. So today we compare an adult's objective results to the objective results of other adults, and determine how intelligent each test taker is compared to all other test takers, instead of comparing test takers to an arbitrary age related standard.
Standard Deviation:
The first step to understanding IQ testing is to understand standard deviation.
Standard deviation is kind of the "avg of the avg," and often can help you find the story behind the data. To understand this concept, it can help to learn about what statisticians call normal distribution of data.
A normal distribution of data means that most of the examples in a set of data are close to the "average," while relatively few examples tend to one extreme or the other.
Let's say you are writing a story about nutrition. You need to look at people's typical daily calorie consumption. Like most data, the numbers for people's typical consumption probably will turn out to be normally distributed. That is, for most people, their consumption will be close to the mean, while fewer people eat a lot more or a lot less than the mean.
When you think about it, that's just common sense. Not that many people are getting by on a single serving of kelp and rice. Or on eight meals of steak and milkshakes. Most people lie somewhere in between.
If you looked at normally distributed data on a graph, it would look something like this:
The x-axis (the horizontal one) is the value in question... calories consumed, dollars earned or crimes committed, for example. And the y-axis (the vertical one) is the number of datapoints for each value on the x-axis... in other words, the number of people who eat x calories, the number of households that earn x dollars, or the number of cities with x crimes committed.
Now, not all sets of data will have graphs that look this perfect. Some will have relatively flat curves, others will be pretty steep. Sometimes the mean will lean a little bit to one side or the other. But all normally distributed data will have something like this same "bell curve" shape.
The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you you have a relatively large standard deviation.
Computing the value of a standard deviation is complicated. But let me show you graphically what a standard deviation represents...
(Niles Online)
One standard deviation away from the mean in either direction on the horizontal axis (the red area on the above graph) accounts for somewhere around 68 percent of the people in this group. Two standard deviations away from the mean (the red and green areas) account for roughly 95 percent of the people. And three standard deviations (the red, green and blue areas) account for about 99
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