Two-sample t test vs. chi-squared test for homogeneity

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SUMMARY

The discussion clarifies the distinct purposes of the two-sample t-test and the chi-squared test for homogeneity. The two-sample t-test is utilized to compare the means of two samples drawn from a normal distribution, while the chi-squared test assesses the similarity of distributions between two populations. It is established that the chi-squared test can be applied in a broader range of situations compared to the t-test, particularly when dealing with categorical data.

PREREQUISITES
  • Understanding of statistical hypothesis testing
  • Familiarity with the two-sample t-test
  • Knowledge of the chi-squared test for homogeneity
  • Basic concepts of normal distribution
NEXT STEPS
  • Study the assumptions and conditions for the two-sample t-test
  • Learn about the application of the chi-squared test in categorical data analysis
  • Explore the differences between parametric and non-parametric tests
  • Investigate the implications of sample size on the validity of both tests
USEFUL FOR

Statisticians, data analysts, researchers, and students seeking to understand the appropriate application of statistical tests for comparing sample means and distributions.

Leo Liu
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The purpose of t test is to find how close the two means of the two samples given are; whereas the result of ##\chi^2## homogeneity test indicates the likeness of the two distributions of two populations (or maybe samples--I am not sure). Can anyone please tell me the differences between them, when one should use which one, and whether one may conclude that chi-squared test is applicable to more situations than t test? Thanks.
 
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I think that your own statement has described the difference between the t-test and the chi-squared test. One thing to add is that the Student's t-test is for a sample drawn from a normal distribution.
 
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