Non-parametric tests are generally applied to analyse
(A) Nominal data
(B) Ordinal data
(C) Interval data
(D) Ratio data
Correct Ans: (A)
Explanation:
Non-parametric tests are statistical methods used to analyze nominal data, which consists of categorical variables without a specific order. These tests do not assume a normal distribution, making them ideal for research involving classifications such as gender, political affiliation, or media preferences. Therefore, researchers use them when data does not meet the assumptions required for parametric tests.
Moreover, nominal data represents categories rather than numerical values. Since it lacks meaningful numerical differences, traditional statistical techniques like mean or standard deviation are not applicable. Consequently, non-parametric tests like the Chi-Square test, Fisher’s Exact test, and McNemar’s test help analyze such data effectively.
In addition, non-parametric tests prove useful in media and communication research. For example, when studying audience preferences for different news channels, researchers classify responses into categories such as “preferred,” “neutral,” and “disliked.” Since these categories lack numerical value, non-parametric tests provide reliable results.
Furthermore, these tests work well when sample sizes are small or when data distribution is unknown. As a result, researchers rely on them to ensure statistical accuracy in qualitative and categorical studies. However, while they offer flexibility, non-parametric methods may lack statistical power compared to parametric tests. Therefore, researchers must choose the appropriate method based on their study design.
To conclude, these tests are essential for analyzing nominal data in research. They provide reliable results when working with categorical variables, making them widely applicable in social sciences and media studies. Therefore, researchers use them to handle non-numeric data effectively.
