chi-square test


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chi-square test

 
a statistical procedure for determining significant differences between frequencies observed within the data and frequencies that were expected. There are two chi-squared tests: the chi-square test of independence, which tests whether two or more series of frequencies are independent of one another; and the chi-square test of goodness of fit, which tests whether an observed frequency distribution fits a specified theoretical model. Written also χ2-test.

chi-square test

a statistical method of assessing the significance of a difference, as when the data from two or more samples, such as the numbers of females and males attending each of two colleges, are represented by a discrete number.
Synonym(s): χ2 test
References in periodicals archive ?
Data from this exercise helps students to understand the relationship between sample size, observed ratios, and power of the chi-square test.
Chi-square tests were used to examine Gender x Career Choice Goals, Ethnicity x Career Choice Goals, Gender x Perceived Barriers, and Ethnicity x Perceived Barriers.
This simple chi-square test is appropriate if multiple admissions are not possible so that the data have a Bernoulli distribution at the individual level.
However, the chi-square test finds no significant difference in the performance of the settlement machinery among the employees.
Table 4: Chi-square tests for Religion-wise inclinations of the students towards tradition Value df Asymp.
The difference between the groups was compared according by Chi-square test and found to be statistically significant (p < 0.
05 (2-tailed), using chi-square test with Yates correction or Fisher exact test, when appropriate.
With regard to Chi-Square test by freedom degree 3 and 4, significance p value<0.
The Pearson Chi-Square test revealed that Vertical parameters play a significant role in the development of Lower incisor crowding (p value = 0.
She explains the purpose of statistical analysis, and details data and distributions, the normal curve, sampling and the Central Limit Theorem, confidence intervals, hypothesis testing, the null hypothesis, analysis of variance, the chi-square test, and correlation and regression.
To test these hypotheses, a chi-square test was run, and the data was analyzed with the use of contingency tables.