I understand that you must be having a lot of questions such as how will you define chi square test? For this reason we have for you the chi square test explained in a very simple way which is understandable so that next time someone will ask you what chi square test meaning is? you can explain them chi square meaning straightforwardly. So let us grab this moment to learn chi square definition.

It measures how expectations are compared to actual observed data. The data we usually use to calculate the static must be random, mutually exclusive and raw. It must be drawn from independent variables and requires the sample which must be large enough.

There are basically two types of chi square method.

The test of independence: This test asks you questions based on relationships such as “Is a relationship between gender and SAT scores exist”?

The goodness-of-fit test: This will ask you questions like “if a coin is being tossed 100 times, is there any chance of 50 time heads and 50 times tails?

With the chi square test table given above and the chi square distribution formula, you can find the answers to your questions:

Chi square distribution formula can be written as:

\[x_{c}^{2} \sum \frac{(O_{i} - E_{1})^{2}}{E_{i}}\]

Where, c is the chi square test degrees of freedom, O is the observed value(s) and E is the expected value(s).

Example: Consider a situation where a random poll of 2,000 different voters, both male and female was taken. The people were classified on the basis of their gender and whether they were democrat, republican, or independent. So the grid will consist of columns labeled as republican, democrat, and independent, whereas two rows labeled as male and female. The data from 2,000 respondents is as follows:

Solution: Our first step to calculate the chi squared statistic will be to find the expected frequencies. The calculation will be made for each "cell" in the grid. Since there are two strata of gender and three categories of political view, we have a total of six expected frequencies. The formula for the expected frequency will be:

E(r, c) = \[\frac{n(r)\times c(r)}{n}\]

Where, r is the row, c is the column and r is the corresponding total.

The expected frequency in this example are:

E(1, 1) = \[\frac{900\times 800}{2000}\] = 360

E(1, 2) = \[\frac{900\times 800}{2000}\] = 360

E(1, 3) = \[\frac{200\times 800}{2000}\] = 80

E(2, 1) = \[\frac{900\times 1200}{2000}\] = 540

E(1, 2) = \[\frac{900\times 1200}{2000}\] = 120

E(2, 3) = \[\frac{200\times 1200}{2000}\] = 120

Now, these are the used values to calculate the chi squared statistic using the following chi square distribution formula:

\[\sum \frac{[O(r, c) - E(r, c)]^{2}]}{E(r, c)}\]

Where, O(r,c) is the observed data for the provided rows and columns.

The expression for each observed value in this example are:

O(1, 1) = \[\frac{[400 - 360]^{2}}{360}\] = 4.44

O(1, 2) = \[\frac{[300 - 360]^{2}}{360}\] = 10

O(1, 3) = \[\frac{[100 - 80]^{2}}{80}\] = 5

O(2, 1) = \[\frac{[500 - 540]^{2}}{540}\] = 2.96

O(2, 2) = \[\frac{[600 - 540]^{2}}{540}\] = 6.67

O(2, 3) = \[\frac{[100 - 120]^{2}}{120}\] = 3.33

So if we equal the sum of these values, it will come upto 32.41. Then we have to look at the chi square test table and find the given chi square test degrees of freedom in our set up to see if the result is statistically significant or not.

FAQ (Frequently Asked Questions)

Question 1) What are the Properties of Chi Square Test?

Answer 1) Chi Square is a tool for testing the relationships between categorical variables in the same population. It measures how expectations are compared to actual observed data.

Some of the properties of chi square distribution are listed below:

The data must be raw, random and mutually exclusive.

The data must consist of independent variables.

The sample drawn must be large enough.

Variance is equal to two times the number of degrees of freedom.

The number of degrees of freedom and the mean distribution are equal to one another.

When the degrees of freedom increases, the normal distribution is approached by the chi square distribution curve.

Question 2) What are a Few Chi Square Test Real Life Example?

Answer 2) The chi-squared distribution emerges out from the estimates of the variance of a normal distribution. It is an approximation to both the distribution of tests of goodness of fit as well as of independence of discrete classifications.

Analysis of variance (for normally distributed data) utilizes the F distribution, which is the ratio of independent chi-square, so even if it isn’t used as a major stepping stone, it is, however, one that we use.

Neither the normal distribution, nor any other really can be considered as a real life example as they are just used as models that are sometimes reasonable approximations.