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- Chi-square is an alternative to traditional t-tests for the one-way ANOVA. Chi-square is a test of whether a particular variable is statistically significant. It is also used for other applications like comparing the relative size of mean or median of two or more groups or to test whether the means of two groups are significantly different. – In contrast, a t-test tests whether the mean of two groups is equal or not. A Chi-square test does not test if the mean of two groups is equal but only if the difference between the mean of two
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Chi-square is an important statistical test that is widely used in many fields. The test is designed to compare means and/or proportions between two populations. Here, I’ll explain step-by-step how to apply Chi-square test for this purpose. The Chi-square test is conducted in two steps: (1) Chi-square calculation and (2) interpretation. Step 1: Calculation of chi-square: The chi-square test can be calculated using the following formula: x² = Sum(1 – [
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It is a well-known fact that in scientific researches, the Chi-square statistics are inevitable. A statistically acceptable chi-square test is an essential step in data analysis. As per Chi-square statistics, the Chi-square test can be performed to compare the frequencies of two independent categories of data in different tables. It may be used for the following purposes: 1. Comparing means: The Chi-square test is commonly used to compare the means of two variables in a study. The chi-square test calculates the probability that the null hypothesis is true