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Introduction to the Normal Distribution (Bell Curve)
1. Statistics
2. Normal Distribution
By Dr. Saul McLeod, published 2019
What are the properties of the normal distribution?
The normal distribution is a continuous probability distribution that is symmetrical on
both sides of the mean, so the right side of the center is a mirror image of the left side.
The area under the normal distribution curve represents probability and the total area
under the curve sums to one.
Most of the continuous data values in a normal distribution tend to cluster around the
mean, and the further a value is from the mean, the less likely it is to occur. The tails are
asymptotic, which means that they approach but never quite meet the horizon (i.e. xaxis).
For a perfectly normal distribution the mean, median and mode will be the same value,
visually represented by the peak of the curve.
The normal distribution is often called the bell curve because the graph of its probability
density looks like a bell. It is also known as called Gaussian distribution, after the
German mathematician Carl Gauss who first described it.
What is the difference between a normal distribution and a standard normal distribution?
A normal distribution is determined by two parameters the mean and the variance. A
normal distribution with a mean of 0 and a standard deviation of 1 is called a standard
normal distribution.
Figure 1. A standard normal distribution (SND).
This is the distribution that is used to construct tables of the normal distribution.
Why is the normal distribution important?
The bell-shaped curve is a common feature of nature and psychology
The normal distribution is the most important probability distribution in statistics because
many continuous data in nature and psychology displays this bell-shaped curve when
compiled and graphed.
For example, if we randomly sampled 100 individuals we would expect to see a normal
distribution frequency curve for many continuous variables, such as IQ, height, weight
and blood pressure.
Parametric significance tests require a normal distribution of the
samples' data points
The most powerful (parametric) statistical tests used by psychologists require data to be
normally distributed. If the data does not resemble a bell curve researchers may have to
use a less powerful type of statistical test, called non-parametric statistics.
Converting the raw scores of a normal distribution to z-scores
We can standardized the values (raw scores) of a normal distribution by converting them
into z-scores.
This procedure allows researchers to determine the proportion of the values that fall
within a specified number of standard deviations from the mean (i.e. calculate the
empirical rule).
Probability and the normal curve: What is the empirical rule formula?
The empirical rule in statistics allows researchers to determine the proportion of values
that fall within certain distances from the mean. The empirical rule is often referred to as
the three-sigma rule or the 68-95-99.7 rule.
If the data values in a normal distribution are converted to standard score (z-score) in a
standard normal distribution the empirical rule describes the percentage of the data that
fall within specific numbers of standard deviations (σ) from the mean (μ) for bell-shaped
The empirical rule allows researchers to calculate the probability of randomly obtaining a
score from a normal distribution.
68% of data falls within the first standard deviation from the mean. This means
there is a 68% probability of randomly selecting a score between -1 and +1
standard deviations from the mean.
95% of the values fall within two standard deviations from the mean. This means
there is a 95% probability of randomly selecting a score between -2 and +2
standard deviations from the mean.
99.7% of data will fall within three standard deviations from the mean. This means
there is a 99.7% probability of randomly selecting a score between -3 and +3
standard deviations from the mean.
How can I check if my data follows a normal distribution?
Statistical software (such as SPSS) can be used to check if your dataset is normally
distributed by calculating the three measures of central tendency. If the mean, median
and mode are very similar values there is a good chance that the data follows a bellshaped distribution (SPSS command here).
It is also advisable to a frequency graph too, so you can check the visual shape of your
data (If your chart is a histogram, you can add a distribution curve using SPSS: From the
menus choose: Elements > Show Distribution Curve).
Normal distributions become more apparent (i.e. perfect) the finer the level of
measurement and the larger the sample from a population.
You can also calculate coefficients which tell us about the size of the distribution tails in
relation to the bump in the middle of the bell curve. For example, Kolmogorov Smirnov
and Shapiro-Wilk tests can be calculated using SPSS.
These tests compare your data to a normal distribution and provide a p-value, which if
significant (p < .05) indicates your data is different to a normal distribution (thus, on this
occasion we do not want a significant result and need a p-value higher than 0.05).
How to reference this article:
McLeod, S. A. (2019, May 28). Introduction to the normal distribution (bell curve). Simply psychology: