Perform statistical hypothesis tests and analyze categorical data with ease using our Chi-Square Calculator.
Our Chi-Square Calculator is designed to help you quickly perform chi-square tests for independence or goodness of fit. Simply input your observed and expected frequencies, and the calculator will compute the chi-square statistic and p-value for you.
Chi-square tests are used to determine if there is a significant relationship between two categorical variables. They're commonly used in research, market analysis, and quality control to assess whether observed frequencies differ significantly from expected frequencies.
A Chi-Square test is a statistical method used to determine if there is a significant relationship between two categorical variables. It compares observed frequencies with expected frequencies to assess whether the differences are due to chance or a real effect.
You should use a Chi-Square test when you have categorical data and want to test for independence between two variables or goodness of fit for a single variable. It's commonly used in research, market analysis, and quality control.
The Chi-Square statistic measures the overall difference between your observed and expected data. A larger value indicates a greater difference between observed and expected frequencies, suggesting a potential relationship between variables.
The p-value in a Chi-Square test represents the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true. A small p-value (typically < 0.05) suggests strong evidence against the null hypothesis.
Chi-Square tests have some limitations: they require a sufficiently large sample size, they're sensitive to sample size (very large samples may show statistical significance for small effects), and they don't provide information about the strength or nature of the relationship between variables.