The area under the function represents the probability of an event occurring in that range. For example, the probability of a student scoring exactly 93.41% on a test is very unlikely. Probability density functions model problems over continuous ranges. For example, 68.3% of the area will always lie within one standard deviation of the mean. The area under the normal distribution is always equal to 1 and is proportional to the standard deviation as shown in the figure below. The standard deviation represents how spread out around the distribution is around the mean. The mean represents the center or 'balancing point' of the distribution. The normal PDF is a bell-shaped probability density function described by two values: the mean and standard deviation. If the cumulative flag is set to FALSE, the return value is equal to the value on the curve. If the cumulative flag is set to TRUE, the return value is equal to the area to the left of the input.
The output of the function is visualized by drawing the bell-shaped curve defined by the input to the function.
