# Chi Square test

The the Chi Square test is used to investigate if distributions of categorical variables differs from one another (Ordinal Scale).
There are 3 different modes in the Chi Square test: These can be selected with the check box.

## Two way count data

To test the difference between to groups of categorical data.

### Interpretation

• The smaller the p value is the more likely there is a significant difference between the 2 data-sets.
• Develve uses the commonly accepted value of p < 0.05 for significance.

### Colors of the cells

• Green
No significant difference
• Yellow
Significant difference

### Formula ### Legend counts expected counts Column Row

### Example

There is no significant difference between the data-sets in column D and E. Data file

## Equal proportions

To test if there is a difference between one of the category and the rest of the data.

### Interpretation

• The smaller the p value is the more likely that at least one proportion in the data set is significant different.
• Develve uses the commonly accepted value of p < 0.05 for significance.

### Colors of the cells

• Green
No significant difference
• Yellow
Significant difference

### Formula sum of counts / amount of options ### Legend counts expected counts

### Example

One of the counts in Column A is significantly different. Data file

## Specific proportions

To test if there is a difference between categorical data and the expected data.

### Interpretation

• The smaller the p value is the more likely there is a significant difference between at least one of the proportion and expected value.
• Develve uses the commonly accepted value of p < 0.05 for significance.

### Colors of the cells

• Green
No significant difference
• Yellow
Significant difference

### Formula sum of counts * percentage (in second column) ### Legend counts expected counts

### Example

There is no significant difference between Column A and the expected ratios. Data file