ChiSquare Goodness of Fit Test  Formula, Guide & Examples
A chisquare (Χ^{2}) goodness of fit test is a type of Pearson’s chisquare test. You can use it to test whether the observed distribution of a categorical variable differs from your expectations.
The chisquare goodness of fit test tells you how well a statistical model fits a set of observations. It’s often used to analyze genetic crosses.
Table of contents
 What is the chisquare goodness of fit test?
 Chisquare goodness of fit test hypotheses
 When to use the chisquare goodness of fit test
 How to calculate the test statistic (formula)
 How to perform the chisquare goodness of fit test
 When to use a different test
 Practice questions and examples
 Other interesting articles
 Frequently asked questions about the chisquare goodness of fit test
What is the chisquare goodness of fit test?
A chisquare (Χ^{2}) goodness of fit test is a goodness of fit test for a categorical variable. Goodness of fit is a measure of how well a statistical model fits a set of observations.
 When goodness of fit is high, the values expected based on the model are close to the observed values.
 When goodness of fit is low, the values expected based on the model are far from the observed values.
The statistical models that are analyzed by chisquare goodness of fit tests are distributions. They can be any distribution, from as simple as equal probability for all groups, to as complex as a probability distribution with many parameters.
Hypothesis testing
The chisquare goodness of fit test is a hypothesis test. It allows you to draw conclusions about the distribution of a population based on a sample. Using the chisquare goodness of fit test, you can test whether the goodness of fit is “good enough” to conclude that the population follows the distribution.
With the chisquare goodness of fit test, you can ask questions such as: Was this sample drawn from a population that has…
 Equal proportions of male and female turtles?
 Equal proportions of red, blue, yellow, green, and purple jelly beans?
 90% righthanded and 10% lefthanded people?
 Offspring with an equal probability of inheriting all possible genotypic combinations (i.e., unlinked genes)?
 A Poisson distribution of floods per year?
 A normal distribution of bread prices?
Flavor  Observed  Expected 
Garlic Blast  22  25 
Blueberry Delight  30  25 
Minty Munch  23  25 
To help visualize the differences between your observed and expected frequencies, you also create a bar graph:
The president of the dog food company looks at your graph and declares that they should eliminate the Garlic Blast and Minty Munch flavors to focus on Blueberry Delight. “Not so fast!” you tell him.
You explain that your observations were a bit different from what you expected, but the differences aren’t dramatic. They could be the result of a real flavor preference or they could be due to chance.
To put it another way: You have a sample of 75 dogs, but what you really want to understand is the population of all dogs. Was this sample drawn from a population of dogs that choose the three flavors equally often?
Here's why students love Scribbr's proofreading services
Chisquare goodness of fit test hypotheses
Like all hypothesis tests, a chisquare goodness of fit test evaluates two hypotheses: the null and alternative hypotheses. They’re two competing answers to the question “Was the sample drawn from a population that follows the specified distribution?”
 Null hypothesis (H_{0}): The population follows the specified distribution.
 Alternative hypothesis (H_{a}): The population does not follow the specified distribution.
These are general hypotheses that apply to all chisquare goodness of fit tests. You should make your hypotheses more specific by describing the “specified distribution.” You can name the probability distribution (e.g., Poisson distribution) or give the expected proportions of each group.
When to use the chisquare goodness of fit test
The following conditions are necessary if you want to perform a chisquare goodness of fit test:
 You want to test a hypothesis about the distribution of one categorical variable. If your variable is continuous, you can convert it to a categorical variable by separating the observations into intervals. This process is known as data binning.
 The sample was randomly selected from the population.
 There are a minimum of five observations expected in each group.
How to calculate the test statistic (formula)
The test statistic for the chisquare (Χ^{2}) goodness of fit test is Pearson’s chisquare:
Formula  Explanation 


The larger the difference between the observations and the expectations (O − E in the equation), the bigger the chisquare will be.
To use the formula, follow these five steps:
Step 1: Create a table
Create a table with the observed and expected frequencies in two columns.
Flavor  Observed  Expected 
Garlic Blast  22  25 
Blueberry Delight  30  25 
Minty Munch  23  25 
Step 2: Calculate O − E
Add a new column called “O − E”. Subtract the expected frequencies from the observed frequency.
Flavor  Observed  Expected  O − E 
Garlic Blast  22  25  22 − 25 = −3 
Blueberry Delight  30  25  5 
Minty Munch  23  25  −2 
Step 3: Calculate (O − E)^{2}
Add a new column called “(O − E)^{2}”. Square the values in the previous column.
Flavor  Observed  Expected  O − E  (O − E)^{2} 
Garlic Blast  22  25  −3  (−3)^{2} = 9 
Blueberry Delight  30  25  5  25 
Minty Munch  23  25  −2  4 
Step 4: Calculate (O − E)^{2} / E
Add a final column called “(O − E)² / E“. Divide the previous column by the expected frequencies.
Flavor  Observed  Expected  O − E  (O − E)^{2}  (O − E)² / E 
Garlic Blast  22  25  −3  9  9/25 = 0.36 
Blueberry Delight  30  25  5  25  1 
Minty Munch  23  25  −2  4  0.16 
Step 5: Calculate Χ^{2}
Add up the values of the previous column. This is the chisquare test statistic (Χ^{2}).
Flavor  Observed  Expected  O − E  (O − E)^{2}  (O − E)^{2 }/ E 
Garlic Blast  22  25  −3  9  9/25 = 0.36 
Blueberry Delight  30  25  5  25  1 
Minty Munch  23  25  −2  4  0.16 
Χ^{2} = 0.36 + 1 + 0.16 = 1.52
How to perform the chisquare goodness of fit test
The chisquare statistic is a measure of goodness of fit, but on its own it doesn’t tell you much. For example, is Χ^{2} = 1.52 a low or high goodness of fit?
To interpret the chisquare goodness of fit, you need to compare it to something. That’s what a chisquare test is: comparing the chisquare value to the appropriate chisquare distribution to decide whether to reject the null hypothesis.
To perform a chisquare goodness of fit test, follow these five steps (the first two steps have already been completed for the dog food example):
Step 1: Calculate the expected frequencies
Sometimes, calculating the expected frequencies is the most difficult step. Think carefully about which expected values are most appropriate for your null hypothesis.
In general, you’ll need to multiply each group’s expected proportion by the total number of observations to get the expected frequencies.
Step 2: Calculate chisquare
Calculate the chisquare value from your observed and expected frequencies using the chisquare formula.
Step 3: Find the critical chisquare value
Find the critical chisquare value in a chisquare critical value table or using statistical software. The critical value is calculated from a chisquare distribution. To find the critical chisquare value, you’ll need to know two things:
 The degrees of freedom (df): For chisquare goodness of fit tests, the df is the number of groups minus one.
 Significance level (α): By convention, the significance level is usually .05.
Step 4: Compare the chisquare value to the critical value
Compare the chisquare value to the critical value to determine which is larger.
Step 5: Decide whether the reject the null hypothesis
 If the Χ^{2} value is greater than the critical value, then the difference between the observed and expected distributions is statistically significant (p < α).
 The data allows you to reject the null hypothesis and provides support for the alternative hypothesis.
 If the Χ^{2} value is less than the critical value, then the difference between the observed and expected distributions is not statistically significant (p > α).
 The data doesn’t allow you to reject the null hypothesis and doesn’t provide support for the alternative hypothesis.
When to use a different test
Whether you use the chisquare goodness of fit test or a related test depends on what hypothesis you want to test and what type of variable you have.
When to use the chisquare test of independence
There’s another type of chisquare test, called the chisquare test of independence.
 Use the chisquare goodness of fit test when you have one categorical variable and you want to test a hypothesis about its distribution.
 Use the chisquare test of independence when you have two categorical variables and you want to test a hypothesis about their relationship.
When to use a different goodness of fit test
The Anderson–Darling and Kolmogorov–Smirnov goodness of fit tests are two other common goodness of fit tests for distributions.
 Use the Anderson–Darling or the Kolmogorov–Smirnov goodness of fit test when you have a continuous variable (that you don’t want to bin).
 Use the chisquare goodness of fit test when you have a categorical variable (or a continuous variable that you want to bin).
Practice questions and examples
Do you want to test your knowledge about the chisquare goodness of fit test? Download our practice questions and examples with the buttons below.
Download Word doc Download Google doc
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.
Statistics
Methodology
Frequently asked questions about the chisquare goodness of fit test
 How do I perform a chisquare goodness of fit test in Excel?

You can use the CHISQ.TEST() function to perform a chisquare goodness of fit test in Excel. It takes two arguments, CHISQ.TEST(observed_range, expected_range), and returns the p value.
 How do I perform a chisquare goodness of fit test in R?

You can use the chisq.test() function to perform a chisquare goodness of fit test in R. Give the observed values in the “x” argument, give the expected values in the “p” argument, and set “rescale.p” to true. For example:
chisq.test(x = c(22,30,23), p = c(25,25,25), rescale.p = TRUE)
 How do I perform a chisquare goodness of fit test for a genetic cross?

Chisquare goodness of fit tests are often used in genetics. One common application is to check if two genes are linked (i.e., if the assortment is independent). When genes are linked, the allele inherited for one gene affects the allele inherited for another gene.
Suppose that you want to know if the genes for pea texture (R = round, r = wrinkled) and color (Y = yellow, y = green) are linked. You perform a dihybrid cross between two heterozygous (RY / ry) pea plants. The hypotheses you’re testing with your experiment are:
 Null hypothesis (H_{0}): The population of offspring have an equal probability of inheriting all possible genotypic combinations.
 This would suggest that the genes are unlinked.
 Alternative hypothesis (H_{a}): The population of offspring do not have an equal probability of inheriting all possible genotypic combinations.
 This would suggest that the genes are linked.
You observe 100 peas:
 78 round and yellow peas
 6 round and green peas
 4 wrinkled and yellow peas
 12 wrinkled and green peas
Step 1: Calculate the expected frequencies
To calculate the expected values, you can make a Punnett square. If the two genes are unlinked, the probability of each genotypic combination is equal.
RY ry Ry rY RY RRYY RrYy RRYy RrYY ry RrYy rryy Rryy rrYy Ry RRYy Rryy RRyy RrYy rY RrYY rrYy RrYy rrYY The expected phenotypic ratios are therefore 9 round and yellow: 3 round and green: 3 wrinkled and yellow: 1 wrinkled and green.
From this, you can calculate the expected phenotypic frequencies for 100 peas:
Phenotype Observed Expected Round and yellow 78 100 * (9/16) = 56.25 Round and green 6 100 * (3/16) = 18.75 Wrinkled and yellow 4 100 * (3/16) = 18.75 Wrinkled and green 12 100 * (1/16) = 6.21 Step 2: Calculate chisquare
Phenotype Observed Expected O − E (O − E)2 (O − E)2 / E Round and yellow 78 56.25 21.75 473.06 8.41 Round and green 6 18.75 −12.75 162.56 8.67 Wrinkled and yellow 4 18.75 −14.75 217.56 11.6 Wrinkled and green 12 6.21 5.79 33.52 5.4 Χ^{2} = 8.41 + 8.67 + 11.6 + 5.4 = 34.08
Step 3: Find the critical chisquare value
Since there are four groups (round and yellow, round and green, wrinkled and yellow, wrinkled and green), there are three degrees of freedom.
For a test of significance at α = .05 and df = 3, the Χ^{2} critical value is 7.82.
Step 4: Compare the chisquare value to the critical value
Χ^{2} = 34.08
Critical value = 7.82
The Χ^{2} value is greater than the critical value.
Step 5: Decide whether the reject the null hypothesis
The Χ^{2} value is greater than the critical value, so we reject the null hypothesis that the population of offspring have an equal probability of inheriting all possible genotypic combinations. There is a significant difference between the observed and expected genotypic frequencies (p < .05).
The data supports the alternative hypothesis that the offspring do not have an equal probability of inheriting all possible genotypic combinations, which suggests that the genes are linked
 Null hypothesis (H_{0}): The population of offspring have an equal probability of inheriting all possible genotypic combinations.
 What are the two main types of chisquare tests?

The two main chisquare tests are the chisquare goodness of fit test and the chisquare test of independence.
 What properties does the chisquare distribution have?

A chisquare distribution is a continuous probability distribution. The shape of a chisquare distribution depends on its degrees of freedom, k. The mean of a chisquare distribution is equal to its degrees of freedom (k) and the variance is 2k. The range is 0 to ∞.
Cite this Scribbr article
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.