Chi-Square Test Calculator
With the Chi-Square Test Calculator you can easily test hypotheses that describe relationships between categorical features (nominal or ordinally scaled).
To calculate a chi-square test, you only need to select two nominal or ordinal variables. DATAtab will then output the Chi2 test calculated online as follows:
Calculate Chi^{2} test from crosstab/contingency table.
You already have a ready-made crosstab and want to calculate the Chi2 test for it? The table in DATAtab assumes that each row is a single case, e.g. a respondent from your survey. From this data DATAtab creates a crosstab or contingency table and calculates the Chi Square test. If you already have a ready-made crosstab, just copy your data into the input field below and click on calculate.
The chi-square statistic is 0.72. The p-value is .697. The result is not significant at p < .05.
Calculate Chi-Squared Test
You can use the Chi-Squared Test Calculator to test hypotheses that describe relationships between categorical characteristics (nominally or ordinally scaled). There are different types of Chi2 test that you can calculate on DATAtab.
If you have one categorical variable, DATAtab will calculate the Goodness of fit Chi2 Test, if you have two categorical variables datatab will calculate the contingency table Chi-Square Test. You can choose from the following test methods:
Chi-squared test for one group
To find out whether a characteristic with more than two states has a certain distribution, select a categorical variable with more than two values.
Chi-squared test for two groups
To check whether the characteristics of two groups are independent of each other, select two categorical variables.
Binomial test calculator
To find out whether a characteristic with two values has a certain distribution, select a categorical variable with two characteristic values.
Chi-Squared Test
The Chi-Squared Test is a statistical test used to determine if there is a significant association between two categorical variables. It compares the observed counts in each category to the counts we would expect to find if there were no association between the variables.
- Type of Data: The Chi-Squared Test is used for categorical data, which means data that can be divided into different categories but not ordered in any meaningful way (e.g., eye color, type of fruit, gender).
- Purpose: It's used to test relationships between categorical variables. For example, you might want to know if there's a relationship between gender and preference for a particular product.
- Test Statistic: The test calculates a value (called the Chi-Squared statistic) which indicates how much the observed counts in your data deviate from what would be expected under the assumption of no association between the variables.
- Expected Counts: The "expected" counts are what you would expect to see in each category if there were no relationship between the two variables. They are calculated based on the overall proportions in the data.
- Example: Imagine a survey where you ask people from two cities (City A and City B) about their favorite fruit (apple or banana). If there's no relationship between city and fruit preference, we'd expect the proportion preferring apples vs. bananas to be roughly the same in both cities. But if City A has way more apple lovers than expected and City B has more banana lovers, the Chi-Squared Test can tell us if this difference is statistically significant.
- Outcome: The test results in a p-value. If the p-value is below a predetermined significance level (commonly 0.05), you would reject the null hypothesis and conclude that there's a significant association between the two categorical variables.
- Assumptions: The test has some assumptions, such as the expected frequency in each category should generally not be too small (commonly suggested as at least 5).
- Variants: There are different types of Chi-Squared Tests. The most common one is the Chi-Squared Test of Independence, but there's also the Chi-Squared Goodness-of-Fit Test, which determines if sample data matches a population distribution.