UGC NET Questions (Paper – 1)

The median is the middle value in an ordered set of scores and is relatively insensitive to extreme values at either end of the distribution. Even if a few scores are very high or very low, the position of the central value remains largely stable, so it often represents central tendency better than the mean in skewed distributions.

Option A:
This option, the arithmetic mean, is calculated by summing all scores and dividing by their number. A few very large or very small values can significantly influence its value, making it sensitive to extremes.

Option B:
This option, the mode, is simply the most frequent score and can be influenced by clustering and irregularities in the data. It may not represent the central point well, especially in continuous or skewed distributions.

Option C:
The geometric mean is still calculated from all scores and, although sometimes less influenced by large values than the arithmetic mean, it can still be distorted by extreme scores. It is not the standard measure chosen purely for resistance to outliers.

Option D:
This option is correct because the median is based only on the order of scores, not their exact magnitude. Extremely high or low values at the tails do not shift the middle position much, which is why the median is least affected by extreme scores.

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 clearly differentiate descriptive from inferential statistics, and F explains how they are used in practice. A is true because descriptive statistics summarise raw data, while B correctly states that inferential statistics support generalisation. C is accurate in classifying central tendency and dispersion as descriptive, and D correctly labels t-tests and ANOVA as inferential tools. F is also true, as most research reports present both descriptive summaries and inferential tests, whereas E is false because descriptive statistics remain important even when inferential methods are applied.

Option A:
Option A omits statement F, ignoring the common practice of combining descriptive and inferential statistics to present a complete analysis. Although A, B, C and D are correct, missing F makes the option incomplete.

Option B:
Option B leaves out statement A and therefore loses the basic definition of descriptive statistics, even though it captures some roles of inferential analysis and joint reporting. Without A, the explanation is not comprehensive, making this option unsuitable.

Option C:
Option C excludes statement B, failing to address the core purpose of inferential statistics: generalising from sample to population. Even with A, C, D and F included, the absence of B means that the description of inferential statistics is incomplete.

Option D:
Option D is correct because it includes A, B, C, D and F, covering both the definitions and typical uses of descriptive and inferential statistics. It excludes E, which incorrectly suggests that descriptive statistics become redundant once inferential statistics are used. Hence, this combination presents all and only the true statements.