Good Essay On Statistical Tests

Chi-square test

The distribution of "chi-square" is most widely used in the statistics for testing statistical hypotheses. On the basis of the distribution of "chi-square" built one of the most powerful tests of fit - the criterion of "chi-square" Pearson. Goodness of fit criterion referred to test the hypothesis proposed law unknown distribution. To verify, we need to compare the empirical (observed) and theoretical (calculated on the assumption of a normal distribution) frequency. It is necessary to say that the main difference between this test from the t-test is that the chi-square test is a nonparametric test.
For example, using the chi-square test can determine the effectiveness of new drugs in the group of subjects. For this it is necessary to fix the frequency distribution of sick and healthy before application and after application of treatment therapy. If the difference in frequencies will be significant, the drug may be effective.

References

Boneau, C. Alan (1960). "The effects of violations of assumptions underlying the t test". Psychological Bulletin 57 (1): 49–64. doi:10.1037/h0041412.
Press, William H.; Saul A. Teukolsky; William T. Vetterling; Brian P. Flannery (1992). Numerical Recipes in C: The Art of Scientific Computing . Cambridge University Press. pp. p. 616 . ISBN 0-521-43108-5.
Edgell, Stephen E., & Noon, Sheila M (1984). "Effect of violation of normality on the t test of the correlation coefficient". Psychological Bulletin 95 (3): 576–583. doi:10.1037/0033-2909.95.3.576.
Nikulin, M.S. (1973). "Chi-square test for normality". In: Proceedings of the International Vilnius Conference on Probability Theory and Mathematical Statistics, v.2, pp. 119–122.
Corder, G.W. & Foreman, D.I. (2014). Nonparametric Statistics: A Step-by-Step Approach. Wiley, New York. ISBN 978-1118840313