A chi square test can be defined as a hypothetical test in statistics, where the sampling distribution of the test statistics is considered, when the null hypothesis comes to be true. This test is used to compare the data with observed data and specify the hypothesis accordingly. For example, if the student is expecting 5 out of 10 offspring to be female and the actual observed female is 3, then the difference between the observed and expected value can be calculated using chi square test. Using this test, the researcher can know how much deviation is occurred before the researcher and conclude with a reason why the observed data is different from the expected. The chi-square test is always used to test the null hypothesis. According to the null hypothesis, there is no significant difference between the observed as well as expected results

**The formula for calculating chi-square is as below –**

*2= (o-e)2/e*

*Where*,

O is the observed data, e is the expected data. The difference is divided by the expected data considering all categories. Most of the **Statistics consultation** firms guide the students to learn the process of Chi-square test. The chi-square test is used by the researchers to determine the difference between the observed and expected data. It also can be used to know about the differences in expected frequencies and observed frequencies in multiple categories. In order to conduct the chi-square test, the research should fulfill the below requirements –

- Quantitative data.
- Multiple categories.
- Separate and individual observations.
- Sufficient sample size
- Random sample.
- Data in the form of frequency
- All the observations along with expected values.

There are different types of chi-square test, which can be classified as below –

**Chi square test for association** – This kind of test is used for analyzing bivariate tabular association. It is non-parametric and is used for nominal data. This kind of test is known as Pearson Chi-Square test and determines whether, different populations differ with each other by considering multiple random samples.

Chi-square goodness-of-fit is another kind of chi-test where the researcher needs to calculate the goodness of the test by comparing expected data with observed data.

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