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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:

Two way count data
Equal proportions
Specific proportions
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
External links