UGC NET Questions (Paper – 1)

A frequency distribution table lists categories, class intervals or distinct scores along with the number of observations in each. It condenses large amounts of raw data into a form that reveals patterns such as concentration, spread and gaps. This table is fundamental to many descriptive statistical procedures. Because the stem describes a table that shows each category or score and the number of cases, frequency distribution is the correct term.

Option A:
Contingency tables display the joint frequencies of two or more categorical variables, showing how categories intersect. While they present frequencies, they focus on associations between variables rather than on the distribution of a single variable. Therefore, contingency table is not the exact match for the stem.

Option B:
A frequency distribution allows quick visualisation of how values are spread across the range and can be converted into graphs such as histograms or bar diagrams. It supports computation of measures like mode and median. These functions fit the description in the question, supporting frequency distribution as the appropriate answer.

Option C:
Cross tabulation is another term frequently used for contingency tables involving two or more variables, not for a simple one-variable frequency table. Since the stem mentions categories or scores of a single variable, cross tabulation is not the most suitable completion.

Option D:
Correlation refers to a statistical measure of the relationship between two quantitative variables, typically summarised by a correlation coefficient. It is not a type of table listing frequencies for categories or scores. Hence, correlation is an incorrect option here.

A frequency distribution lists the distinct values or categories of a variable along with the number of observations in each. It is a fundamental tool in descriptive statistics because it summarises large datasets into a simple, interpretable form. This table can be presented in absolute frequencies, relative frequencies or percentages. Therefore, a table showing how many cases fall into each category is correctly called a frequency distribution.

Option A:
In a frequency distribution, categories may be individual values or class intervals, and the table can be used to create graphs such as histograms or bar charts. It helps identify patterns like skewness, modality and concentration of scores. These characteristics match the description given in the stem.

Option B:
A scatter diagram plots paired scores for two variables on a coordinate plane to visualise their relationship, not the count of cases within categories of a single variable. It does not take the form of a simple table of frequencies. Hence, scatter diagram is not the appropriate completion.

Option C:
A regression line summarises the linear relationship between two quantitative variables in a scatter plot and is used for prediction. It is an analytic tool, not a tabular display of frequencies for categories of a single variable. Therefore, regression line does not fit the question.

Option D:
A contingency table cross-classifies cases into categories of two or more variables simultaneously, showing joint frequencies. Although it is related to frequency distributions, the stem refers specifically to counts for each category of one variable, so contingency table is not the best answer here.

Statements A, B, C and D correctly describe the main measures used in descriptive statistics. Means, medians and modes summarise central tendency in different ways, while variance and standard deviation capture variability. Statement E is false because descriptive statistics summarise data but do not by themselves conduct hypothesis tests; that is the role of inferential statistics. Therefore, the combination that includes A, B, C and D but excludes E is correct.

Option A:
Option A is correct because it recognises the four key descriptive indicators and their appropriate use, while rejecting the claim that they automatically perform inferential functions. It preserves the conceptual distinction between describing and testing.

Option B:
Option B is incomplete because it omits D, thereby failing to mention how dispersion measures contribute to understanding the spread of data. Without D, the description of descriptive statistics is focused only on central tendency.

Option C:
Option C is incorrect because it leaves out A, not explicitly acknowledging the suitability of the mean for interval and ratio data, which is essential in many analyses. This omission makes the set partial.

Option D:
Option D is wrong because it includes E, the assertion that descriptive statistics automatically test hypotheses, which confuses their function with inferential procedures.