Shapes of distribution

Distribution of data can take a wide variety of shapes, and ultimately depends on how data points are distributed along the measurement scale. A general "feel" for the data can be achieved by examining the uniformity (or lack thereof) of a distribution. In general, the larger the sample size, the more symmetrical the distribution.

Normal (bell-shaped) distribution

When the collected data tends to hover around a central value, with no bias to the left or the right, the data creates a Normal distribution. This Normal distribution is also referred to as the "Bell Curve" because it resembles a bell like shape.

When stating that data is normally distributed we are identifying that 50% of the values are less than the mean and that 50% of the values are greater than the mean. In a normal distribution the mean, median, and mode are equal to one another.

Examples of data that follow a normal distribution could include blood pressure and scores on a test.

contributed by, Scott Trungadi

Skewness

Skewed right Skewed left

Acceptable skewness values: -1.000 < skewness < 1.000

Examining the data skewness allows you to see the variability of a data set. Skewness is when a data set does not follow the normal distribution. A normal distribution has a skewness of zero, and will have perfect symmetry. Data that is positively skewed will be skewed to the right and will be a positive number; data that is negatively skewed is skewed to the left of the data mean, and is a negative number. See an example of skewness, below.

"contributed by Cassandra Cosentino"

Kurtosis

Leptokurtic Platykurtic

Acceptable kurtosis values : -1.000 < kurtosis < 1.000