What is Normal Distribution and Why is it Important?

How to Determine if a Data Set is Normally Distributed

One way to determine if a data set is normally distributed is to plot the data on a graph and see if it follows a bell curve. If it does not, then the data set may not be normally distributed.

Another way to determine if a data set is normally distributed is to use the Kolmogorov-Smirnov test. This test compares two distributions of data and determines which distribution has more probability of being normal. If it turns out that one distribution has more probability of being normal than another, then we can conclude that the first distribution has more likelihood of being normal than the second.

What are the Disadvantages of Normal Distribution?

A normal distribution is a probability distribution that has the following properties:

1) It is completely specified by its mean and variance.

2) The probability density function is symmetric about the mean.

3) It has a single mode at the mean.

4) The total area under the curve equals 1.

The disadvantages of normal distribution are that it lacks predictability, it is not flexible enough to accommodate other types of data sets and it can lead to false conclusions.

How do I Graph a Normal Distribution?

A normal distribution is a probability distribution that has a bell-shaped curve. It is one of the most common distributions in statistics. The average, or mean, of the distribution is at the peak of the curve and it narrows as you move towards the edges.

The first step in graphing a normal distribution is to find out where it should peak by using a histogram. Histograms are graphs that show how many occurrences there are for each value in a data set. The histogram for this data set would have two bars because there are two values in it: 10 and 15. The height of each bar represents how many occurrences there are for that specific value.