The normal (Gaussian) distribution is the most important of all the distributions, continuous or otherwise. Its graph is symmetric, bell-shaped, and unimodal. It is widely used and even more widely abused. You see this distribution in almost all disciplines, including psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some of your instructors may use the normal distribution to help determine your grade. In both the natural world and in human society, many elements—from IQ scores to real estate prices—fit a normal distribution.

The normal distribution has two parameters (i.e., two numerical descriptive measures): the mean (μ) and the standard deviation (σ). If X is a quantity to be measured that has a normal distribution with mean (μ) and standard deviation (σ), we designate this by writing X ~ N(μ, σ).

The probability density function of this curve is as follows:

f(x) =

where:

• -∞ < X < ∞
• -∞ < μ < ∞
• σ > 0

As you can see, the normal PDF is a rather complicated function. This could be a problem since the normal distribution is so widely used. However, we will see some ways we can work around this.

The cumulative distribution function is P(X ≤ x). It can be calculated either by calculus, technology, or a table (though technology has made tables almost obsolete).

The curve is symmetric about a vertical line drawn through the mean, μ. In theory, the mean is the same as the median, because the graph is symmetric about μ. As the notation indicates, the normal distribution depends only on the mean and the standard deviation. Since the area under the curve must equal one, a change in the standard deviation, σ, causes a change in the shape of the curve, which becomes fatter or skinnier depending on σ. A change in μ causes the graph to shift to the left or right. This means there are an infinite number of normal probability distributions. One of special interest is called the standard normal distribution.

The Empirical Rule

The place to start when working with the normal distribution is the empirical rule. It applies to any normal distribution or data that has a bell-shaped, symmetric curve. According to the rule, if X is a random variable and has a normal distribution with mean µ and standard deviation σ, then:

• Approximately 68% of the values of x are within one standard deviation of the mean (±σ or z-scores of ±1)
• Approximately 95% of the values of x are within two standard deviations of the mean (±2σ or z-scores of ±2)
• Approximately 99.7% of the values of x are within three standard deviations of the mean (±3σ or z-scores of ±3)

The empirical rule is also known as the 68-95-99.7 rule.

Finding Normal Probabilities

The shaded area in the following graph indicates the area to the left of x. This area is represented by the probability P(X < x).

If we know the area to the left, we can then use the complement rule to find the area to the right:

P(X > x) = 1 – P(X < x)

To find the area between two numbers, we can write the equation in terms of a CDF:

P(a < X < b) = P(X < b) – P(X < a)

Also recall that for continuous distributions:

P(X < x) ≅ (X ≤ x) & P(X > x) ≅ P(X ≥ x)

There are three main ways we could find probabilities associated with the normal distribution:

• Complicated math
• The standardizing process
• Technology

If you recall the formula previously presented for the PDF of the normal distribution, you could imagine why it’s preferable to avoid involving complicated math if possible.

In order to work around that, there is a process called standardizing that involves z-scores, the standard normal distribution, and tables. Although this tried and true process is now somewhat antiquated, it is a great place to start.

There are many technologies (e.g., calculators and various pieces of statistical software) that let us skip the entire standardizing process and instantaneously provide us with a probability. Although we typically have these at our disposal to use in practice, it is good to understand the process going on behind the scenes to make sure we apply our technology correctly.

The Standard Normal Distribution

The standard normal distribution (SND) is the simplest form of the normal distribution. The mean for the standard normal distribution is zero, and the standard deviation is one. The transformation z = produces the distribution Z ~ N(0, 1). The value x in the given equation comes from a normal distribution with mean μ and standard deviation σ.

Recall our previous discussion of z-scores, which are converted to units of the standard deviation. If X is a normally distributed random variable and X ~ N(μ, σ), then the z-score is:

Recall a z-score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z-scores, and values of x that are smaller than the mean have negative z-scores. If x equals the mean, then x has a z-score of zero.

We have the z-table at our disposal with probabilities already calculated and organized. Note that most z-tables give us the left-tailed, CDF, or “less than” probability. For example, the area to the left of a z-score of -3.37 is P(Z ≤ -3.37) = 0.0004.

The SND CDF value, P(Z ≤ z), is also denoted as Φ(z). We can then use these CDF values, P(Z ≤ z), and some probability rules to find greater than [P(Z ≥ z) = 1-P(Z ≤ z)] or in-between [P(a ≤ Z ≤ b) = P(Z ≤ b) – P(Z ≤ a)] probabilities.

The Standardizing Process

So far, we have discussed converting any normal distribution with any mean and standard deviation to the standard normal distribution in units of z-scores. We also have the associated probabilities in our z-table. Essentially, the work has been done for us if we know how to standardize and look up the associated probability in the table. The general process is:

X ~ N(μ, σ) -> Z` ~ N(0, 1) -> probability from z-table

While maybe outdated in our technology age, this process is good for beginners to understand and useful when we do not have access to technology.

Working Backwards

Sometimes, we may be given a percentile or z-score and want to work backward through the standardizing process to find a value on the original distribution. This “un-standardizing” process of finding a normal quantile or percentile associated with the normal distribution looks like this:

Probability in z-table -> Z ~ N(0, 1) -> X ~ N(μ, σ)

For example, if the mean of a normal distribution is five and the standard deviation is two, what value is three standard deviations above (to the right of) the mean (z-score = 3). Rearranging the z-score formula, the calculation is as follows:

x = μ + (z)(σ) = 5 + (3)(2) = 11

Often, we are given a percentile to find on the original distribution. For example, what if we want to know a value on the previous distribution that corresponds to the 90th percentile? We can look up a probability of 0.9 in the z-table and find a corresponding z-score of approximately 1.28.

x = μ + (z)(σ) = 5 + (1.28)(2) = 7.56

Figure References

Figure 4.19: Kindred Grey (2020). Normal distribution. CC BY-SA 4.0.

Figure 4.20: Kindred Grey (2020). Empirical rule. CC BY-SA 4.0.

Figure 4.21: Kindred Grey (2020). P(X < x). CC BY-SA 4.0.

Figure 4.22: Kindred Grey (2020). Z-table. CC BY-SA 4.0.