Match the following statistical test to their application: 

List I	List II
(a) T test	(i) Prediction
(b) X2 test	(ii) Comparison of means of two groups
(c) Correlation	(iii) Observed Vs. expected frequencies
(d) Multiple regression	(iv) Covariance

Codes:	(a)	(b)	(c)	(d)
(A)	(iii)	(ii)	(i)	(iv)
(B)	(ii)	(iii)	(iv)	(i)
(C)	(i)	(iv)	(iii)	(ii)
(D)	(iv)	(i)	(ii)	(iii)

Correct Ans: (C)

Explanation:

Statistical tests play a crucial role in research. They help analyze data, identify patterns, and make informed decisions. Each test serves a specific purpose, making it essential to choose the right one based on the research objective.

1. T-Test – Comparison of Means of Two Groups
   The T-test is widely used to compare the means of two independent or related groups. Researchers apply this test to determine whether differences between groups occur by chance or due to an actual effect. For instance, it helps compare audience engagement between two different media campaigns.
2. Chi-Square (X²) Test – Observed vs. Expected Frequencies
   The Chi-square test examines relationships between categorical variables. It helps determine whether an observed distribution significantly differs from an expected one. Media researchers, for example, use this test to analyze voting patterns across different demographics.
3. Correlation – Covariance
   Correlation measures the strength and direction of a relationship between two variables. It helps understand how changes in one factor influence another. For instance, researchers use it to examine the connection between social media usage and news consumption.
4. Multiple Regression – Prediction
   Multiple regression assesses how multiple independent variables predict a dependent variable. It is useful in media research for understanding how various factors, like education, income, and media exposure, influence audience preferences.

Overall, these statistical tests enhance research accuracy. They provide meaningful insights, helping scholars draw valid conclusions.