The Normal CurveThe normal curve, or normal probability curve, represents a distribution of scores in a sample or population. In statistics, it rules! It is the foundation of statistical analysis--averages, comparisons, associations, and more. Even percentiles are based on this distribution.
The Normal Distribution Curve
Both sides of the normal bell curve are equal and symmetrical. If you could pick up one side and place it on the other, they would match perfectly.
The tails at either end never touch the baseline. This allows no "ceiling" or "floor" limits for statistical outcomes, so probabilities are infinite. The mean, median, and mode (see Measures of Central Tendency) fall at the middle of this distribution. You typically need a large enough group or number of scores to achieve a bell-shaped distribution. When outliers, or extreme scores, occur the distribution becomes skewed and the curve is no long identical on both sides. In this case, the mean is pulled toward one tail. Think of the smart kid who "messed up the curve" for everyone. Standard deviations (SD) on the curve are particularly important and useful. SDs represent specific percentages of scores on the distribution. For example, if your blood work is "within normal limits", none of your reading fall outside of 2 SDs of the population. You are among 95% of people on important health indicators. The applications of these tools are endless. Knowing how to use them is the key to successfully applying statistics to real world decision making. Related pages: The Skewed Distribution Standard Scores Statistical Analysis
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