CHI SQUARE TEST Meaning and
Definition
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The chi-square test is a statistical method used to determine the independence or association between categorical variables. It is a non-parametric test that compares observed frequencies with expected frequencies to assess if there is a significant difference between them.
In the chi-square test, data is organized into a contingency table, which is a cross-tabulation of the variables. The observed frequencies are the actual counts of data points in each category, while the expected frequencies are the counts that would be expected if the variables were independent. The chi-square statistic is then calculated by summing the squared difference between observed and expected frequencies, divided by expected frequencies.
The resulting chi-square statistic value is compared to a critical value from the chi-square distribution table based on the degrees of freedom (df), which is calculated as (number of rows - 1) x (number of columns - 1) in the contingency table. If the calculated chi-square value exceeds the critical value at a chosen significance level (commonly 0.05), it indicates that there is a significant association or dependency between the variables.
The chi-square test is widely used in various fields such as social sciences, biology, and healthcare research to analyze relationships between categorical variables. It helps researchers determine if there is a meaningful association between variables, which can aid in making informed decisions or drawing conclusions from the data.
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