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    Choose the correct answer from the options given below:
    Question

    Match the LIST-I with LIST-II:


    Choose the correct answer from the options given below:

    A.

    A-III, B-IV, C-II, D-I

    B.

    A-I, B-II, C-IV, D-III

    C.

    A-III, B-II, C-IV, D-I

    D.

    A-I, B-II, C-III, D-IV

    Correct option is A

    1. ANOVA Test (A-III) - Correct

    ANOVA (Analysis of Variance) is a statistical test used to determine whether there are significant differences between the means of three or more independent groups. It helps in drawing inferences about whether the samples have been drawn from the same population or not. This is why it is correctly matched with III.

    2. T-Test (B-IV) - Correct

    The T-test is applicable when the sample size is small (n ≤ 30). It is used when comparing the means of two groups, and when the population standard deviation is unknown. Therefore, it is correctly matched with IV.

    3. Z-Test (C-II) - Correct

    The Z-test is used for hypothesis testing when the sample size is large (n > 30) and the population variance is known. It is correctly matched with II.

    4. Goodness of Fit Test (D-I) - Correct

    The Goodness of Fit Test is used to check if a sample distribution fits a theoretical distribution. It determines the validity of observed data regarding the assumption about the distribution of a population, making it correctly matched with I.

    Thus, the correct matching is A-III, B-IV, C-II, D-I.

    Information Booster:

    Statistical tests are essential in hypothesis testing and research. ANOVA is widely used in experimental designs, especially in business, medicine, and social sciences, where multiple groups need to be compared. The T-test is critical for small sample analysis and is commonly used in academic research and A/B testing. The Z-test is frequently applied in large-scale studies, ensuring accurate statistical inferences. Lastly, the Goodness of Fit Test plays a significant role in evaluating whether observed data conforms to a theoretical model, making it useful in areas like genetics, economics, and quality control. Understanding these tests helps researchers make sound, data-driven decisions.

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