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Chi – square test

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Dr.M.Prasad Naidu
Dr.M.Prasad Naidu

The document discusses the chi-square test, which offers an alternative method for testing the significance of differences between two proportions. It was developed by Karl Pearson and follows a specific chi-square distribution. To calculate chi-square, contingency tables are made noting observed and expected frequencies, and the chi-square value is calculated using the formula. Degrees of freedom are also calculated. Chi-square test is commonly used to test proportions, associations between events, and goodness of fit to a theory. However, it has limitations when expected values are less than 5 and does not measure strength of association or indicate causation.

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M.Prasad Naidu MSc Medical Biochemistry, Ph.D,. .
Introduction Chi-square test offers an alternate method of testing the significance of difference between two proportions. Chi-square test involves the calculation of chi-square. Chi-square is derived from the greek letter ‘chi’ (X). ‘Chi’ is pronounced as ‘Kye’. Chi-square was developed by Karl pearson.
Chi-square test is a non-parametric test. It follows a specific distribution known as Chi-square distribution. Calculation of Chi-square value The three essential requirements for Chi-square test are: A random sample Qualitative data Lowest expected frequency not less than 5
The calculation of Chi-square value is as follows: - Make the contingency tables - Note the frequencies observed (O) in each class of one event, row-wise and the number in each group of the other event, column-wise. - Determine the expected number (E) in each group of the sample or the cell of table on the assumption of null hypothesis.
- The hypothesis that there was no difference between the effect of the two frequencies, and then proceed to test the hypothesis in quantitative terms is called the Null hypothesis. - Find the difference between the observed and the expected frequencies in each cell (O – E). - Calculate the Chi-square values by the formula - Sum up the Chi-square values of all the cells to get the total Chi-square value.

Chi – square test
Chi – square test
- Calculate the degrees of freedom which are related to the number of categories in both the events. - The formula adopted in case of contingency table is Degrees of freedom (d.f.) = (c – 1 ) (r – 1) Where c is the number of columns and r is the number of rows
Applications of Chi-square Chi-square test is most commonly used when data are in frequencies such as the number of responses in two or more categories. Chi-square test is very useful in research. The important applications of Chi-square in medical statistics are : - Test of proportion - Test of association - Test of goodness of fit
- Test of proportion It is an alternate test to find the significance of difference in two or more than two proportions. Chi-square test is applied to find significance in the same type of data with two more advantages, - to compare the values of two binomial samples even if they are small. - to compare the frequencies of two multinomial samples.
- Test of association Test of association is the most important application of Chi- square test in statistical methods. Test of association between two events in binomial or multinomial samples is measured. Chi-square test measures the probability of association between two discrete attributes.
- Test of Goodness of fit Chi-square test is also applied as a test of “goodness of ‘ fit”. Chi-square test is used to determine if actual numbers are similar to the expected or theoretical numbers – goodness of fit to a theory.
Restrictions (limitations) in application of Chi-square test The Chi-square test is applied in a four fold table will not give a reliable result with one degree of freedom if the expected value in any cell is less than 5. The Chi-square test does not measure the strength of association. The statistical finding of relationship, does not indicate the cause and effect.
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Chi – square test

  • 1. M.Prasad Naidu MSc Medical Biochemistry, Ph.D,. .
  • 2. Introduction Chi-square test offers an alternate method of testing the significance of difference between two proportions. Chi-square test involves the calculation of chi-square. Chi-square is derived from the greek letter ‘chi’ (X). ‘Chi’ is pronounced as ‘Kye’. Chi-square was developed by Karl pearson.
  • 3. Chi-square test is a non-parametric test. It follows a specific distribution known as Chi-square distribution. Calculation of Chi-square value The three essential requirements for Chi-square test are: A random sample Qualitative data Lowest expected frequency not less than 5
  • 4. The calculation of Chi-square value is as follows: - Make the contingency tables - Note the frequencies observed (O) in each class of one event, row-wise and the number in each group of the other event, column-wise. - Determine the expected number (E) in each group of the sample or the cell of table on the assumption of null hypothesis.
  • 5. - The hypothesis that there was no difference between the effect of the two frequencies, and then proceed to test the hypothesis in quantitative terms is called the Null hypothesis. - Find the difference between the observed and the expected frequencies in each cell (O – E). - Calculate the Chi-square values by the formula - Sum up the Chi-square values of all the cells to get the total Chi-square value.
  • 6.
  • 9. - Calculate the degrees of freedom which are related to the number of categories in both the events. - The formula adopted in case of contingency table is Degrees of freedom (d.f.) = (c – 1 ) (r – 1) Where c is the number of columns and r is the number of rows
  • 10. Applications of Chi-square Chi-square test is most commonly used when data are in frequencies such as the number of responses in two or more categories. Chi-square test is very useful in research. The important applications of Chi-square in medical statistics are : - Test of proportion - Test of association - Test of goodness of fit
  • 11. - Test of proportion It is an alternate test to find the significance of difference in two or more than two proportions. Chi-square test is applied to find significance in the same type of data with two more advantages, - to compare the values of two binomial samples even if they are small. - to compare the frequencies of two multinomial samples.
  • 12. - Test of association Test of association is the most important application of Chi- square test in statistical methods. Test of association between two events in binomial or multinomial samples is measured. Chi-square test measures the probability of association between two discrete attributes.
  • 13. - Test of Goodness of fit Chi-square test is also applied as a test of “goodness of ‘ fit”. Chi-square test is used to determine if actual numbers are similar to the expected or theoretical numbers – goodness of fit to a theory.
  • 14. Restrictions (limitations) in application of Chi-square test The Chi-square test is applied in a four fold table will not give a reliable result with one degree of freedom if the expected value in any cell is less than 5. The Chi-square test does not measure the strength of association. The statistical finding of relationship, does not indicate the cause and effect.