Understanding the Difference Between a Chi-Square Test and a T-Test.
Understanding the Difference Between a Chi-Square Test and a T-Test.
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The chi-square test is a statistical test used to determine the relationship between categorical variables.
The chi-square test is a statistical test used to determine the relationship between categorical variables.
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The chi-square test measures the difference between observed and expected frequencies.
The chi-square test measures the difference between observed and expected frequencies.
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Statisticians use the t-test to compare the means of two groups of continuous variables.
Statisticians use the t-test to compare the means of two groups of continuous variables.
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A test statistic and a p-value can be obtained by the chi-square test.
A test statistic and a p-value can be obtained by the chi-square test.
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The t-test estimates the difference in means between two data sets, with a higher test statistic indicating greater variability.
The t-test estimates the difference in means between two data sets, with a higher test statistic indicating greater variability.
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Numerous group analyses can be conducted with both chi-square tests and t-tests.
Numerous group analyses can be conducted with both chi-square tests and t-tests.
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Click here to learn more about Understanding the Difference Between a Chi-Square Test and a T-Test.
Click here to learn more about Understanding the Difference Between a Chi-Square Test and a T-Test.