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The chi-square statistic compares the observed values to the expected values. This test statistic is used to determine whether the difference between the observed and expected values is statistically significant.
The chi-square statistical test is named after the Greek letter chi, which is the symbol 2, used to represent this statistic in formulas and results. Chi-square is the usual test of the null hypothesis for differences between the frequencies that are observed and the expected frequencies.
Is a 0.05 P-Value Significant? A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
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A p-value of less than or equal to 0.05 is regarded as evidence of a statistically significant result, and in these cases, the null hypothesis should be rejected in favor of the alternative hypothesis.
Interpret the key results for Chi-Square Test for Association Step 1: Determine whether the association between the variables is statistically significant. Step 2: Examine the differences between expected counts and observed counts to determine which variable levels may have the most impact on association.
If your chi-square calculated value is greater than the chi-square critical value, then you reject your null hypothesis. If your chi-square calculated value is less than the chi-square critical value, then you fail to reject your null hypothesis.

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