UGC NET Questions (Paper – 1)

A ratio scale has all the properties of an interval scale—equal intervals between values—and, in addition, a true zero point that indicates the complete absence of the measured attribute. This allows statements such as one value being twice or half of another to be meaningful. Examples include height, weight and reaction time. Because the stem highlights equal intervals, a true zero and meaningful ratios, it is describing a ratio scale.

Option A:
Interval scales, such as temperature in Celsius, have equal intervals but lack a true zero point, which makes ratio statements (like “twice as hot”) conceptually problematic. Therefore, interval is not sufficient to capture the “true zero and meaningful ratios” criterion given in the question.

Option B:
Ratio scales permit the full range of mathematical operations, including addition, subtraction, multiplication and division. This rich set of properties enables powerful statistical analyses and interpretable comparisons. These features match the description in the stem, so ratio is the correct completion.

Option C:
Nominal scales classify data into categories without order or quantitative meaning, making arithmetic operations and ratio statements inappropriate. They lack both equal intervals and a true zero, so nominal cannot be the answer.

Option D:
Ordinal scales indicate order among categories but do not assume equal differences between ranks, and they lack a true zero point. While they allow ranking, they do not support meaningful ratio comparisons, making ordinal an incorrect option for the stem’s description.

Statements A, B, C and D correctly distinguish parametric and non-parametric tests. Parametric tests rest on stronger distributional assumptions and are suited to interval or ratio data, while non-parametric tests are more flexible and can be used for ordinal data or when assumptions are violated. Statement E is false because the appropriateness and accuracy of a test depend on how well its assumptions fit the data; non-parametric tests are not automatically superior. Thus, the set containing A, B, C and D is the only correct combination.

Option A:
Option A includes A, B and C but omits D, failing to note that non-parametric procedures are particularly useful when parametric assumptions do not hold or when dealing with ordinal data. Without D, the practical role of non-parametric tests is understated.

Option B:
Option B lists B, C and D but omits A, so it does not explicitly state that parametric methods assume particular distributions such as normality. This omission weakens the conceptual contrast between the two types of tests.

Option C:
Option C contains A, C and D but leaves out B, thereby not acknowledging that non-parametric tests make fewer assumptions about population form. Since B is a central descriptive point, its absence makes this combination incomplete.

Option D:
Option D is correct because it combines all four accurate statements and excludes E, which exaggerates the superiority of non-parametric methods. It presents a balanced view of when each class of tests is appropriate.

Statements A, B, C and E accurately describe key features of parametric and non-parametric tests, whereas statement D is incorrect. Statement A is true because most parametric tests presuppose that the underlying population is normally distributed or meets similar assumptions. Statement B is correct since non-parametric tests are commonly used for nominal and ordinal data, and statement C rightly notes that parametric procedures generally require information about population parameters such as mean and standard deviation. Statement E is also true because the decision between parametric and non-parametric tests depends on both the level of measurement and distributional characteristics, while statement D is false since non-parametric tests are not automatically applicable without regard to assumptions and are not always more powerful than parametric tests.

Option A:
Option A contains A, B and C, all of which are correct statements, but it omits E. Because E is also a true statement about how researchers choose between parametric and non-parametric tests, this option does not include the complete set of correct statements. Therefore it cannot be the right answer.

Option B:
Option B includes B, C and E but leaves out A. While B, C and E are true, excluding A ignores the important assumption that parametric tests usually require normality or similar distributional properties. Since all four of A, B, C and E are correct, this incomplete combination is not acceptable.

Option C:
Option C groups A, C and E and omits B. Although A, C and E are correct, B is also a true statement describing when non-parametric tests are typically employed. Leaving out a correct statement means the combination is not fully accurate, so this option is invalid.

Option D:
Option D correctly lists A, B, C and E while excluding D, which is the only false statement. It acknowledges the assumptions behind parametric tests, the typical uses of non-parametric tests, the need for parameter information and the role of measurement levels and distribution in test selection. Because it contains all and only the true statements, this option is the correct answer.

A ratio scale possesses all the properties of an interval scale—equal units and meaningful differences—plus a true zero point that indicates the complete absence of the quantity being measured. Because of the true zero, statements such as one value being twice another are meaningful. Examples include height, weight and Kelvin temperature. Thus, a scale with equal units and a true zero that allows ratio comparisons is correctly called a ratio scale.

Option A:
Nominal scales classify objects into categories without any inherent order, such as gender or blood group. They do not have equal units or a zero point with quantitative meaning, so they do not match the stem.

Option B:
Ordinal scales rank order items but do not guarantee equal intervals between ranks; they allow comparison of “greater than” or “less than” but not meaningful differences or ratios. Hence, ordinal is not the right answer.

Option C:
Interval scales have equal units and allow meaningful differences to be calculated, but they lack a true zero, so ratios can be misleading. Celsius temperature is a classic example. Therefore, interval does not fulfil the true-zero condition in the stem.

Option D:
Option D, ratio scale, uniquely combines equal intervals with an absolute zero, enabling multiplication and division operations that are not valid on other scales. This matches the description in the question precisely.

A nominal scale involves categorising observations into distinct groups that have no inherent order, such as gender, religion or blood type. The categories are mutually exclusive and exhaustive but cannot be ranked meaningfully. Only counts and mode are appropriate descriptive measures. Therefore, classifying respondents by gender uses a nominal scale.

Option A:
Nominal measurement simply names or labels categories without implying that one category is greater or less than another. In the gender example, categories like male, female or other do not fall into a natural order, confirming that the scale is nominal and making this option correct.

Option B:
An ordinal scale ranks categories in terms of order, such as low, medium and high, but does not guarantee equal intervals; this is not what is happening in simple gender classification.

Option C:
Interval scales have equal intervals and allow meaningful differences, as in temperature measured in degrees, but do not apply to purely categorical variables like gender.

Option D:
Ratio scales add a true zero point and support meaningful ratios, such as weight or height; these properties are irrelevant to categorising gender, so ratio is not the correct answer.