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.

Statements A, B, C, D and F correctly describe aspects of quantitative data analysis, while E is false. Statement A is true because descriptive statistics use measures like mean and standard deviation, and statement B correctly notes that inferential statistics enable generalisation. Statement C is accurate since graphs such as histograms summarise distributions, and statement D is true because data often need to be coded before software analysis. Statement F is also correct in stating that test choice depends on measurement level and research questions, whereas E is false because even rigorous quantitative analysis cannot guarantee correctness if design is flawed.

Option A:
Option A includes only A, B and C, omitting D and F. While the included statements are correct, leaving out the need for coding and the dependence of test choice on measurement level and questions results in an incomplete description. Therefore this option cannot be the correct answer.

Option B:
Option B adds D but still omits F, failing to mention that test selection depends on measurement scales and the nature of questions. Although it captures more aspects than Option A, it still does not include all the true statements from the list.

Option C:
Option C contains B, C, D and F but omits A. Since A is a foundational statement about descriptive statistics, excluding it weakens the completeness of the combination. A correct answer must encompass all the true statements A, B, C, D and F.

Option D:
Option D correctly groups A, B, C, D and F and excludes E, which is the only false statement. It recognises both descriptive and inferential aspects, the role of coding, graphical summaries and principled test selection. As it contains all and only the true statements, this option is the correct answer.

Skewness indicates whether a distribution is symmetric around the mean or whether one tail is stretched out more than the other. Positive skewness means the tail is longer on the right side with more high-end outliers, while negative skewness means the tail is longer on the left. Assessing skewness helps determine whether parametric tests that assume normality are appropriate or whether transformations might be needed. Therefore, the statistic described in the stem is correctly called skewness.

Option A:
Kurtosis refers to the peakedness or flatness of a distribution relative to normal, not the degree of asymmetry between tails. Although related to distribution shape, it addresses a different aspect than the one in the stem.

Option B:
Skewness summarises how scores pile up toward one side of the mean and taper off on the other, affecting measures like mean and standard deviation. When skewness is large, median and non-parametric methods may give more robust results. These features match the description of asymmetry in the stem, so this option is correct.

Option C:
Variability is a general term for spread in data and is quantified by measures like variance and standard deviation; it does not specifically capture tail asymmetry.

Option D:
Central tendency statistics such as mean or median describe typical values, not the balance of tails, so central tendency is not the correct term here.

Quantitative research relies on numerical data that must be organized and interpreted systematically. Statistical techniques allow researchers to describe patterns, relationships and variability in the data. Through procedures such as estimation, significance testing and confidence intervals, researchers can test hypotheses and draw inferences about populations from samples. Thus statistical analysis is central to quantitative data interpretation.

Option A:
Option A minimizes the use of numerical structure by emphasizing personal impressions, which undermines objectivity. While interpretation is necessary, it should be grounded in statistical evidence.

Option B:
Option B correctly states that quantitative analysis uses statistics for both description and inference. It captures the dual roles of summarizing data and testing hypotheses, reflecting standard practices in quantitative methodology.

Option C:
Option C suggests ignoring numerical patterns, which defeats the purpose of collecting quantitative data. Qualitative description has its place but cannot substitute for statistical analysis in quantitative studies.

Option D:
Option D indicates selective use of data to support preconceived opinions, which is unethical and methodologically flawed. Proper analysis should consider all relevant data, not just convenient subsets.

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.

Statistical analysis in quantitative research serves several purposes. Descriptive statistics summarize data through measures like mean, median and standard deviation. Inferential statistics help examine relationships and differences, and test hypotheses about populations based on sample data. Together, these techniques provide an evidence based basis for interpreting findings.

Option A:
Option A suggests replacing numbers with only qualitative description, which ignores the advantages of numerical precision and patterns that statistics reveal.

Option B:
Option B is correct because it combines summarization, exploration of relationships and hypothesis testing, which are central tasks of quantitative data analysis. It recognizes that statistics support but do not replace substantive interpretation.

Option C:
Option C claims that statistics eliminate the need for interpretation, which is false. Researchers must still relate numerical results to theory, context and practical implications.

Option D:
Option D assumes automatic acceptance of hypotheses, whereas statistical testing may lead to either rejection or failure to reject, depending on evidence.

A bar diagram represents categorical data with rectangular bars whose lengths are proportional to the frequencies or percentages of categories. The bars are of equal width and are separated by gaps to emphasise that the categories are distinct and not on a continuous scale. Bar diagrams are commonly used to display nominal or ordinal data. Therefore, the graph described in the stem is correctly called a bar diagram.

Option A:
A histogram also uses bars, but they touch each other because they represent continuous data grouped into intervals, indicating no gaps between values. This distinguishes histograms from bar diagrams.

Option B:
A line graph connects data points with lines to show trends over time or ordered categories; it does not use separate bars with gaps to depict frequencies.

Option C:
A pie chart divides a circle into sectors whose angles represent the proportions of categories, and although useful, it is visually quite different from bars of equal width and gaps.

Option D:
Bar diagrams are particularly effective for comparing categories at a glance and are easily interpreted even by non-experts. These features correspond to the description in the question, making this option correct.

Standard deviation is defined as the positive square root of the variance and summarises how far, on average, scores deviate from the mean. A larger standard deviation indicates greater dispersion, while a smaller one suggests that scores cluster closely around the mean. It is widely used in both descriptive and inferential statistics to quantify variability and to compute standard errors. Therefore, the spread measure described in the stem is known as the standard deviation.

Option A:
The standard deviation provides a measure in the same units as the original scores, making it easier to interpret than the variance. It plays a central role in normal distribution theory and confidence interval estimation. These properties align precisely with what the question is describing.

Option B:
The mean is the arithmetic average (a measure of central tendency), not a measure of dispersion computed as the square root of variance. It tells where the centre of the distribution lies, but it does not quantify spread around the mean. Therefore, mean does not fit the stem.

Option C:
Quartiles divide an ordered dataset into four parts, and measures based on quartiles (like interquartile range) describe spread differently. “Quartile” itself is not defined as the square root of variance, so it cannot complete the term “_____ deviation” correctly here.

Option D:
The coefficient of variation is the standard deviation divided by the mean (often expressed as a percentage) and is used to compare relative variability across different scales. It is derived from standard deviation rather than being the square root of variance itself. Hence, it is not the correct completion.

The mean is calculated by summing all scores and dividing by the number of observations, so every value contributes to its magnitude. As a result, extremely high or low scores can pull the mean toward them, distorting its representation of the typical value. This sensitivity makes the mean less robust in skewed distributions. Because the stem asks for the measure most affected by extreme scores, mean is the correct answer.

Option A:
Median is based on the middle position of ordered scores and is largely unaffected by the exact values of extremes, as long as their positions do not change. It is therefore more robust to outliers than the mean. Hence, median does not match the property described in the stem.

Option B:
Mean provides useful information in symmetric distributions with few outliers but becomes misleading when the data are highly skewed, such as income distributions. In such cases, median or trimmed means are often preferable. The strong impact of extreme scores on mean corresponds to the statement in the question, confirming mean as the appropriate completion.

Option C:
Mode identifies the most frequent score in the distribution and is not calculated using all values. While the presence of outliers can alter frequencies, extreme scores are not inherently more influential than other scores. Thus, mode is not the measure that is most affected by extremes in the sense described.

Option D:
Geometric mean is used mainly for multiplicative processes or growth rates and, although sensitive in its own way, is not the standard measure considered in discussions about the influence of extreme scores on central tendency in basic descriptive statistics. Therefore, geometric mean is not the best answer here.

Measures of dispersion indicate the extent to which scores differ from each other and from the central tendency. Standard deviation summarizes the average distance of scores from the mean; a larger value implies greater variability, while a smaller one indicates tighter clustering.

Option A:
This option is correct because standard deviation directly quantifies spread around the mean. It is routinely reported in research articles to describe variability and underpins many inferential statistics.

Option B:
This option, the arithmetic mean, is a measure of central tendency, not dispersion. It reports the average level of scores but not how spread out they are.

Option C:
The median is also a measure of central tendency. It indicates the middle score but does not describe whether the other scores are close together or widely scattered.

Option D:
The mode shows which score occurs most frequently, but it gives no information about how far other observations deviate from that value, so it is not a measure of dispersion.

The median is the middle value of an ordered data set, with an equal number of observations lying below and above it. It is especially useful when the distribution is skewed or when extreme values might distort the mean. As a positional measure, it depends on the order of scores rather than their magnitude. Because the stem describes a value that divides the distribution into two equal halves, median is the correct term.

Option A:
The median is determined by arranging the scores in ascending or descending order and identifying the central position, or averaging the two central values when the number of observations is even. It remains unaffected by extreme scores because it depends only on relative position. These characteristics fit the description in the question, confirming median as the right completion.

Option B:
Mode is the score that occurs most frequently in a distribution and may not coincide with the point that divides the distribution into two equal halves. A distribution can have more than one mode or even no clear mode. Thus, mode is not what the stem is defining.

Option C:
Mean is the arithmetic average, found by adding all scores and dividing by the number of scores. It takes every value into account and is sensitive to extreme scores, but it is not the score where 50 percent of observations lie below and 50 percent above. Therefore, mean is not the correct answer here.

Option D:
Midrange is calculated as the average of the minimum and maximum values in the distribution and may not reflect the central tendency accurately if extreme scores are present. It is not defined as the point that splits the distribution into two equal halves. Hence, midrange is not the appropriate completion.

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.

Statements A, B, C, E and F correctly describe capabilities and good practices for online surveys, while D is false. Automation facilitates wide geographical reach, basic analysis tools summarise responses, and consent information can be embedded at the start of a form. Thoughtful distribution supports appropriate sampling, and export functions enable deeper analysis in specialised software. Since sampling bias and low response rates remain real risks, D cannot be accepted, leaving A, B, C, E and F as the correct set.

Option A:
Option A is correct because it combines statements that acknowledge both technical features and methodological responsibilities. It excludes D’s unrealistic claim that online surveys are immune to common research limitations.

Option B:
Option B is incorrect because it includes D and omits E and F. Accepting D would mean ignoring well-known challenges of online data collection, and omitting E and F underplays careful distribution and advanced analysis.

Option C:
Option C is incomplete because it omits A and includes only B, C, E and F. While those statements are true, excluding A fails to mention the fundamental advantage of remote data collection through online tools.

Option D:
Option D is incorrect because it accepts D, which states that biases and low responses never occur. This contradicts standard research methodology, so any combination containing D is invalid.

Standard deviation quantifies the spread of scores around the mean by taking the square root of the average of squared deviations. A larger standard deviation indicates that scores are more widely dispersed, while a smaller one shows they cluster closely around the mean. It is widely used because it relates directly to variance and plays a central role in many statistical procedures. Therefore, the measure described in the stem is correctly called the standard deviation.

Option A:
Standard deviation provides a more informative picture of variability than the range because it considers all data points, not just extremes. It is also compatible with normal distribution theory and confidence intervals, making it a fundamental descriptive statistic. These properties align with the stem’s focus on average deviation from the mean, so this option is correct.

Option B:
The mean is a measure of central tendency, representing the average score, and does not describe how far scores typically lie from that average.

Option C:
The mode is the most frequently occurring value in a distribution and gives no information about overall dispersion.

Option D:
The range is the difference between the highest and lowest scores and is sensitive only to extreme values, not to the overall spread, so it does not match the stem.

Statements A, B, C and E correctly describe basic statistical ideas, while D is false. The mean is influenced by extreme values, the median resists them better, standard deviation measures dispersion and in a normal distribution the three central tendency measures coincide. Effect size indeed reflects how large a difference or association is, complementing significance tests. However, a small p-value does not necessarily imply a large effect; with a big sample even tiny effects can be statistically significant.

Option A:
Option A omits E, ignoring the important interpretive role of effect size in judging the practical importance of findings. Without E, the explanation remains focused only on descriptive statistics and neglects the link to substantive significance.

Option B:
Option B leaves out B, so it fails to mention standard deviation, a key descriptor of variability, even though it comments on mean, median and effect size. This omission makes the option incomplete.

Option C:
Option C excludes A, thereby not recognising the difference in sensitivity to outliers between mean and median, which is a core interpretive issue. As a result, it cannot be accepted.

Option D:
Option D is correct because it combines all true statements about measures of central tendency, variability and effect size, while rejecting D, which mistakenly equates small p-values with large effects. It encourages a nuanced interpretation of statistical results.

Option E contains D, the false claim that a small p-value guarantees a large effect size, and so any combination including it becomes incorrect, even if other parts are true.