Types of Chi-Square Tests
The two types of chi-square tests use the chi-square statistic and distribution for different purposes. These two tests are:
- The chi-square goodness of fit test
This is a nonparametric test that strives to find out how the observed value of a given event differs significantly from the expected value. The term goodness of fit is used in this context to mean a comparison of the observed sample distribution and the expected probability distribution. The chi-square goodness of fit test help statisticians understand how well theoretical distribution fits the empirical distribution. To perform this test, divide the sample data into intervals then compare the number of points that fall into the interval with the number of points in each interval that you are expecting.
- Chi-square test of independence
This type of test checks for a relationship between two variables that are in a contingency table. In simple terms, it tests whether distributions of categorical variables differ from one another. In a contingency table (two-way table) data is classified according to two categorical variables. One category appears in rows while the other in columns. You should note that the chi-square test of independence only works for categorical variables. You cannot use it to compare continuous variables or continuous and categorical variables. Also, this type of test cannot provide inferences about causation.
The Chi-square statistic
We use the formula below when performing a chi-square test.
In the formula:
- The subscript c denotes the degrees of freedom
- O is the value that has been observed
- E is the value that is expected
- The summation symbol tells you to perform a calculation for every single data item in your set of data
Rarely, you will manually use this formula to compute a vital chi-square value by hand. This is because the calculations can be lengthy and tedious. Instead, competent researchers now opt for technology like the chi-square test in SPSS and chi-square p-value n Excel.
Chi-square P -Values
A p-value from a chi-square test tells the researcher if the results from the test are significant or not. Before you perform a chi-square test and get the p-value, you will need two vital pieces of information:
- The degrees of freedom. This refers to the number of categories minus one
- The alpha level. This level is chosen by the person carrying out the test. In most cases, the alpha level is 5%. However, there are also levels like 0.01 or 0.10.
These two pieces of information will be given to you in a question when you are studying AP statistics or elementary statistics. Do you need assistance with your Chi-square test task? Get in touch with us now.