{"id":216255,"date":"2024-09-28T11:04:47","date_gmt":"2024-09-28T11:04:47","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/what-p-value-do-we-use-for-chi-square-test\/"},"modified":"2024-09-28T11:04:47","modified_gmt":"2024-09-28T11:04:47","slug":"what-p-value-do-we-use-for-chi-square-test","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/what-p-value-do-we-use-for-chi-square-test\/","title":{"rendered":"What P value do we use for chi-square test?"},"content":{"rendered":"

A chi-square test is a statistical test used to determine if there is a significant association between two categorical variables. The test calculates a chi-square statistic and a corresponding p-value to evaluate the hypothesis that there is no association between the variables. The p-value determines the statistical significance of the test.<\/p>\n

In a chi-square test, the p-value represents the probability of obtaining the observed data or more extreme data, assuming that the null hypothesis is true (i.e., assuming that there is no association between the variables). If the p-value is less than a predetermined significance level (usually 0.05 or 0.01), it is considered statistically significant, and we reject the null hypothesis in favor of the alternative hypothesis, indicating that there is evidence of an association between the variables. On the other hand, if the p-value is greater than the significance level, we fail to reject the null hypothesis, suggesting that there is not enough evidence to conclude an association.<\/p>\n

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