Match the tests with suitable situations of their application for hypothesis-testing:

	Test		Situation
(a)	z-test	(i)	Comparing the differences in the mean  values of more than two sample groups  (Parametric data)
(b)	ANOVA	(ii)	Testing the significance of the difference  between averages of two large-sized sample  groups (Parametric data)
(c)	Chi-Square Test	(iii)	Comparing the differences in the mean values  of more than two sample groups (Non -   parametric data)
d)	Kruskal - Wallis	(iv)	Testing the significance of the association  between two attributes

 
Choose the correct option from those given below 

Test

Situation

 z-test

1.  A z-test is a statistical test to determine whether two population means are different when the variances are known and the sample size is large.
2. It can be used to test hypotheses in which the z-test follows a normal distribution. 
3. Testing the significance of the difference
    between averages of two large-sized sample
    groups (Parametric data)

 ANOVA

1.  Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample.
2. Comparing the differences in the mean
    values of more than two sample groups
    (Parametric data)

 Chi-Square Test

1. The Chi-Square Test of Independence determines whether there is an association between categorical variables (i.e., whether the variables are independent or related).
2. It is a nonparametric test.
3. This test is also known as the Chi-Square Test of Association.
4. Testing the significance of the association
    between two attributes

 Kruskal - Wallis

1.  The Kruskal-Wallis H test (sometimes also called the "one-way ANOVA on ranks") is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable.
2. Comparing the differences in the mean values
    of more than two sample groups.