Chi-Square (Χ²) Distributions | Definition & Examples

A chi-square (Χ2) distribution is a continuous probability distribution that is used in many hypothesis tests.

The shape of a chi-square distribution is determined by the parameter k. The graph below shows examples of chi-square distributions with different values of k.

Chi-square Distribution k

What is a chi-square distribution?

Chi-square (Χ2) distributions are a family of continuous probability distributions. They’re widely used in hypothesis tests, including the chi-square goodness of fit test and the chi-square test of independence.

The shape of a chi-square distribution is determined by the parameter k, which represents the degrees of freedom.

Very few real-world observations follow a chi-square distribution. The main purpose of chi-square distributions is hypothesis testing, not describing real-world distributions.

In contrast, most other widely used distributions, like normal distributions or Poisson distributions, can describe useful things such as newborns’ birth weights or disease cases per year, respectively.

Relationship to the standard normal distribution

Chi-square distributions are useful for hypothesis testing because of their close relationship to the standard normal distribution. The standard normal distribution, which is a normal distribution with a mean of zero and a variance of one, is central to many important statistical tests and theories.

Imagine taking a random sample of a standard normal distribution (Z). If you squared all the values in the sample, you would have the chi-square distribution with k = 1.

Χ21 = (Z)2

Now imagine taking samples from two standard normal distributions (Z1 and Z2). If each time you sampled a pair of values, you squared them and added them together, you would have the chi-square distribution with k = 2.

Χ22 = (Z1)2 + (Z2)2

More generally, if you sample from k independent standard normal distributions and then square and sum the values, you’ll produce a chi-square distribution with k degrees of freedom

Χ2k = (Z1)2 + (Z2)2 + … + (Zk)2

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Chi-square test statistics (formula)

Chi-square tests are hypothesis tests with test statistics that follow a chi-square distribution under the null hypothesis. Pearson’s chi-square test was the first chi-square test to be discovered and is the most widely used.

Pearson’s chi-square test statistic is:

Formula Explanation
 $X^2 = \sum {\frac {(O - E)^2}{E}}$ Where

  • X² is the chi-square test statistic
  • $\sum$ is the summation operator (it means “take the sum of”)
  • $O$ is the observed frequency
  • $E$ is the expected frequency

If you sample a population many times and calculate Pearson’s chi-square test statistic for each sample, the test statistic will follow a chi-square distribution if the null hypothesis is true.

The shape of chi-square distributions

We can see how the shape of a chi-square distribution changes as the degrees of freedom (k) increase by looking at graphs of the chi-square probability density function. A probability density function is a function that describes a continuous probability distribution.

When k is one or two

When k is one or two, the chi-square distribution is a curve shaped like a backwards “J.” The curve starts out high and then drops off, meaning that there is a high probability that Χ² is close to zero.

Chi-square Distribution k=1

When k is greater than two

When k is greater than two, the chi-square distribution is hump-shaped. The curve starts out low, increases, and then decreases again. There is low probability that Χ² is very close to or very far from zero. The most probable value of Χ² is Χ² − 2.

When k is only a bit greater than two, the distribution is much longer on the right side of its peak than its left (i.e., it is strongly right-skewed).

Chi-square Distribution k=3

As k increases, the distribution looks more and more similar to a normal distribution. In fact, when k is 90 or greater, a normal distribution is a good approximation of the chi-square distribution.

Chi-square Distribution k = 90

Properties of chi-square distributions

Chi-square distributions start at zero and continue to infinity. The chi-square distribution starts at zero because it describes the sum of squared random variables, and a squared number can’t be negative.

The mean (μ) of the chi-square distribution is its degrees of freedom, k. Because the chi-square distribution is right-skewed, the mean is greater than the median and mode. The variance of the chi-square distribution is 2k. 

Properties of chi-square distributions
Property Value
Continuous or discrete Continuous
Mean k
Mode k − 2 (when k > 2)
Variance 2
Standard deviation $\sqrt{2k}$
Range 0 to ∞
Symmetry Asymmetrical (right-skewed), but increasingly symmetrical as k increases.

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Example applications of chi-square distributions

The chi-square distribution makes an appearance in many statistical tests and theories. The following are a few of the most common applications of the chi-square distribution.

Pearson’s chi-square test

One of the most common applications of chi-square distributions is Pearson’s chi-square tests. Pearson’s chi-square tests are statistical tests for categorical data. They’re used to determine whether your data are significantly different from what you expected. There are two types of Pearson’s chi-square tests:

Example: Pearson’s chi-square test
A company that sells shirts wants to know if all their shirt colors are equally popular, so they record the number of sales per shirt color for one week.

Number of sales per shirt color
Color Frequency
Red 30
Gray 29
Yellow 26
Pink 33
Black 56
White 90
Blue 86

Since there were 350 shirt sales in total, 50 sales per color would be exactly equal. It’s obvious that there weren’t exactly 50 sales per color. However, this is just a one-week sample, so we should expect the numbers to be a little unequal just due to chance.

Does the sample give enough evidence to conclude that the frequency of shirt sales truly differs between shirt colors?

A chi-square goodness of fit test can test whether the observed frequencies are significantly different from equal frequencies. By comparing Pearson’s chi-square test statistic to the appropriate chi-square distribution, the company can calculate the probability of these shirt sale values (or more extreme values) happening due to chance.

Population variance inferences

The chi-square distribution can also be used to make inferences about a population’s variance (σ²) or standard deviation (σ). Using the chi-square distribution, you can test the hypothesis that a population variance is equal to a certain value using the test of a single variance or calculate confidence intervals for a population’s variance.

Example: Test of a single variance
A large union wants to ensure that all workers with the same seniority are receiving similar salaries. Their goal is a standard deviation in hourly salary that’s less than $2.

To test whether they’ve achieved their goal, the union randomly selects 30 workers with the same seniority. It finds that the standard deviation of the sample is $1.98. This is very slightly less than $2, but it’s just a sample. Is it enough evidence to conclude that the true standard deviation of all workers with the same seniority is less than $2?

The union can use the test of a single variance to find out whether the standard deviation (σ) is significantly different from $2.

By comparing a chi-square test statistic to the appropriate chi-square distribution, the union can decide whether to reject the null hypothesis.

F distribution definition

Chi-square distributions are important in defining the F distribution, which is used in ANOVAs.

Imagine you take random samples from a chi-square distribution, and then divide the sample by the k of the distribution. Next, you repeat the process with a different chi-square distribution. If you take the ratios of the values from the two distributions, you will have an F distribution.

The non-central chi-square distribution

The non-central chi-square distribution is a more general version of the chi-square distribution. It’s used in some types of power analyses.

The non-central chi-square distribution has an extra parameter called λ (lambda) or the non-central parameter. This parameter changes the shape of the distribution, shifting the peak to the right and increasing the variance as λ increases.

Chi-square Distribution k=5

The λ parameter works by defining the mean of the normal distributions that underlie the chi-square distribution. For example, you can produce a non-central chi-square distribution with λ = 2 and k = 3 by squaring and summing values sampled from three normal distributions, each with a mean of two and a variance of one.

Other interesting articles

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about chi-square distributions

What happens to the shape of the chi-square distribution as the degrees of freedom (k) increase?

As the degrees of freedom (k) increases, the chi-square distribution goes from a downward curve to a hump shape. As the degrees of freedom increases further, the hump goes from being strongly right-skewed to being approximately normal.

What properties does the chi-square distribution have?

A chi-square distribution is a continuous probability distribution. The shape of a chi-square distribution depends on its degrees of freedom, k. The mean of a chi-square distribution is equal to its degrees of freedom (k) and the variance is 2k. The range is 0 to ∞.

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Shaun Turney

During his MSc and PhD, Shaun learned how to apply scientific and statistical methods to his research in ecology. Now he loves to teach students how to collect and analyze data for their own theses and research projects.