chi-square test

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statistics, science of collecting and classifying a group of facts according to their relative number and determining certain values that represent characteristics of the group. The most familiar statistical measure is the arithmetic mean, which is an average value for a group of numerical observations. A second important statistic or statistical measure is the standard deviation, which is a measure of how much the individual observations are scattered about the mean. The chi-square test is a method of determining the odds for or against a given deviation from expected statistical distribution. Other statistics indicate other characteristics of the group of observations. In addition to the problem of computing certain statistics for a particular group of observations, there is the problem of sampling. This is an attempt to determine for what larger group (called the population) of individuals or characteristics the statistics for this particular group (called the sample) would be a representative figure and how representative a figure it would be for a given larger group. This second problem of sampling can be solved only by resorting to the theory of probability and higher mathematics. In most applications of statistics to scientific and social research, insurance, and finance, the statistician is interested not only in the characteristics of the sample but also in those of some much larger population. Consequently, the theory of sampling is the most important part of statistical theory.


See J. F. Freund, Modern Elementary Statistics (1988); D. S. Moore and G. P. McCabe, Introduction to the Practice of Statistics (1989); D. H. Sanders, Statistics (1989).

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chi-square test

[′kī ¦skwer ′test]
A generalization, and an extension, of a test for significant differences between a binomial population and a multinomial population, wherein each observation may fall into one of several classes and furnishes a comparison among several samples instead of just two.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
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Hyperuricaemic patients significantly developed more hypertensive retinopathy in comparison to normouricaemic patients (* p<0.02; ** chi square test).
When chi square test was applied to these ratings it showed the presence of statistical significance with (p=0.001) between the three groups that is Dentists-Orthodontists-Lay persons.
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The one-way chi square test confirms that these results are statistically significant ([chi square] = 80.03, df = 4, [rho] [less than or equal to] .0001).
Barnett suggests using a chi square test to make this process more objective.
First, to determine the overall effectiveness of the SPS in identifying high risk patients, a one-group chi square test was employed comparing base line suicidal frequency data with observed numbers of suicidal incidents.
The relation between the primary and secondary objectives was studied using the Chi square test, with the help of Software, SPSS.
In order to determine if the observed differences in performance of activities were statistically significant, the chi square test of independence was used.
The second section concentrates on experimental design, principles, and procedures including linear models and analysis of variance, method and layout of Completely Randomized Design, the advantages and disadvantages of Randomized Complete Block Design, and the Chi Square Test of Significance.
As the socio-demographic characteristics of non-respondent caregivers were unknown, characteristics of patients with respondent caregivers and patients with non-respondent caregivers were obtained from the patient's medical record and were analyzed for statistical significance using a Chi Square Test of Independence (Table 2).