The Normal Distribution

compiled by Metty Agustine Primary

Characteristics of the Normal Distribution

• Symmetrical means it has a bell-shaped
• Unimodal  means it has the mean, median and mode which are the same
• Asymptotic means the upper and lower tails of the distribution never touch the baseline of the X axis. In other words, it has no zero score.

Why is the Normal Distribution So Important?

• The three characteristics of the normal distribution are each critical in statistics because they allow us to make good use of probability statistics.
• The normal distribution can provide a researcher to be able to make inferences about the population based on the data collected from the sample.
•  Thus, inferential statistics are used to determine whether some issues or phenomenon observed in a sample represents an actual issue in the population from which the sample was drawn.

Skew and Kurtosis

• Skew happens when a distribution of data set does not have the bell-shaped. In other words, it is not normal. There are two kinds of skewed distribution – positively and negatively skewed.
• Kurtosis  refers to a measure of peaked-ness or flatness of the distribution. A highly peaked distribution will have a positive value of kurtosis; a flat distribution will have a negative value of kurtosis. 
• A normal distribution will have a kurtosis value of zero or close to zero.