When conducting a Chi Square test, it is important to have the right type of data. The Chi Square test is a statistical test used to determine if there is a significant association between two categorical variables. This test compares observed frequencies with expected frequencies to determine if there is a significant difference.

**Types of Data:**

There are two types of data that are needed for a Chi Square test: observed frequencies and expected frequencies.

__Observed Frequencies:__

The observed frequencies are the actual counts or numbers that you have collected or obtained from your data. These frequencies represent the number of occurrences or instances for each category or level of your variables. For example, if you are testing the association between gender and preference for a certain product, your observed frequencies would be the number of males and females who prefer or do not prefer the product.

__Expected Frequencies:__

The expected frequencies are the frequencies that you would expect to see if there was no association between the variables. These frequencies are calculated based on certain assumptions and expectations. The calculation of expected frequencies depends on the total sample size and the distribution of categories within each variable.

**Data Format:**

To conduct a Chi Square test, it is important to organize your data in a specific format. The data should be arranged in a contingency table or cross-tabulation table, which displays the frequency distribution of one variable across different categories of another variable.

**Contingency Table Example:**

## Table 1: Preference for Product by Gender

Preference | Total | ||
---|---|---|---|

Gender | Preference A | Preference B | |

Male | 20 | 30 | 50 |

Female | 40 | 10 | 50 |

Total | 60 | 40 | 100 |

In the example above, the contingency table shows the frequency distribution of preference for a product (Preference A or Preference B) across different genders (Male or Female). The observed frequencies are represented by the counts in each cell of the table.

**Note:**

It is important to ensure that your data meets certain assumptions before conducting a Chi Square test. These assumptions include independence of observations, expected frequencies greater than or equal to 5 in each cell, and random sampling.

### In conclusion,

To conduct a Chi Square test, you need both observed frequencies and expected frequencies organized in a contingency table format. This test helps determine if there is a significant association between two categorical variables. By understanding the type of data needed and organizing it properly, you can effectively analyze and interpret the results of a Chi Square test.

*Happy analyzing!*