UGC NET Questions (Paper – 1)

Analysis of variance (ANOVA) is used when a researcher wishes to compare the means of three or more groups simultaneously. It partitions total variability into components due to between-group and within-group differences and computes an F-ratio to test the null hypothesis that all group means are equal. If the F-test is significant, it suggests that at least one mean differs from the others. Thus, the technique described in the stem is correctly called analysis of variance.

Option A:
The chi-square test is used mainly for categorical data to test independence or goodness-of-fit, not to compare means of several groups on a quantitative variable. Therefore, it is not the appropriate method here.

Option B:
Correlation analysis measures the strength and direction of the linear relationship between two quantitative variables. It does not directly test differences among multiple group means. So it does not fit the description.

Option C:
Option C, analysis of variance, can handle one-way or multi-factor designs and is fundamental in experimental research with several conditions. Its specific purpose is comparing multiple means, matching the stem exactly and making this option correct.

Option D:
Regression analysis models the relationship between a dependent variable and one or more predictors, focusing on prediction and explanation rather than simple mean comparisons. Although related mathematically to ANOVA, it is not the name of the test described in the question.

A Type I error occurs when a researcher concludes that an effect or difference exists in the population when in fact the null hypothesis is true. It is analogous to a false positive decision. The probability of committing a Type I error is controlled by the significance level alpha, such as 0.05. Therefore, the error of rejecting a true null hypothesis described in the stem is called a Type I error.

Option A:
Type II errors occur in the opposite situation, when a false null hypothesis is not rejected, corresponding to a false negative; this is not the scenario given.

Option B:
Random error refers broadly to unpredictable fluctuations in data due to chance influences, not specifically to the decision error of rejecting a true null hypothesis.

Option C:
Systematic error involves consistent bias in measurement or procedure, which affects estimates but does not directly define the statistical decision about hypotheses.

Option D:
Type I errors are a central concern in hypothesis testing because setting a low alpha reduces their likelihood, though it may increase Type II errors. This link with rejecting true null hypotheses fits the stem exactly, making this option correct.

Deductive reasoning proceeds from the general to the specific. In research, it usually starts with an existing theory or general principle and then derives specific hypotheses that can be tested empirically, allowing researchers to check whether observations support or contradict theoretical expectations.

Option A:
This option correctly describes deduction as starting with a general theoretical framework and narrowing down to specific statements about what should be observed. It reflects the classic sequence used in many quantitative studies.

Option B:
This option illustrates inductive reasoning, where patterns and theories emerge from specific observations; the researcher builds generalizations after examining particular classroom situations.

Option C:
Option C is another example of induction. Generating categories from interview transcripts without prior assumptions involves moving from detailed qualitative data to broader concepts or themes.

Option D:
Option D also reflects an inductive, exploratory approach in which the researcher enters the field without a guiding theory and allows patterns to emerge from the data, rather than deriving specifics from a prior general principle.

A Type I error, also known as alpha error, happens when the null hypothesis is actually true in the population but the sample evidence leads the researcher to reject it. In other words, the test concludes that an effect or difference exists when in reality it does not. The probability of committing this error is controlled by the chosen significance level, such as 0.05.

Option A:
Option A describes failing to reject a false null hypothesis, which is a Type II error (false negative), not Type I.

Option B:
Option B matches the formal definition of Type I error: incorrect rejection of a true null hypothesis, leading to a false positive conclusion.

Option C:
Option C refers to accepting (or failing to reject) a true null hypothesis, which is the correct decision and not an error.

Option D:
Option D portrays correct rejection of a false null hypothesis, which is the desired outcome of hypothesis testing rather than an error.

A Type I error occurs when a statistical test leads the researcher to reject the null hypothesis even though it is actually true in the population. This is also called an alpha error and represents a false positive conclusion. The probability of committing a Type I error is controlled by the chosen significance level, such as 0.05. Hence, the situation described in the stem is correctly termed a Type I error.

Option A:
A Type II error happens when the test fails to reject the null hypothesis even though it is false, leading to a false negative conclusion. This concerns lack of power rather than over-detection of effects, so it does not match the definition in the question.

Option B:
Sampling error refers to natural fluctuations between sample statistics and population parameters due to observing only a subset of the population. It is a broader concept and is not specifically about the incorrect rejection of a true null hypothesis. Therefore, sampling error is not the correct completion.

Option C:
Systematic error is a consistent bias in measurement or procedure that skews results in one direction, often threatening validity, but it is not the specific decision error defined in statistical hypothesis testing. Thus, systematic error is not appropriate here.

Option D:
Type I errors are particularly serious in contexts where wrongly claiming an effect can lead to unnecessary or harmful interventions, which is why researchers often set conservative significance levels. This role fits exactly with the stem’s description of rejecting a true null hypothesis.

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.

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.

Statements A, B, D, E and F correctly explain aspects of significance testing, while C is wrong. Statement A is true because the null hypothesis typically asserts no difference or relationship, and statement B is correct that a small p-value suggests the data are unlikely under the null. Statement D rightly notes that not rejecting the null does not prove it true, and statement E is correct in pointing out that large samples can make small effects significant. Statement F is also true because practical interpretation requires attention to effect size and context, whereas C is false because statistical significance does not guarantee practical importance.

Option A:
Option A labels C and D as wrong, but D is actually a correct caution about interpreting non-rejection of the null. Misclassifying D as wrong makes this combination conceptually flawed.

Option B:
Option B treats A and C as wrong, but A is a standard definition of the null hypothesis and is therefore correct. By grouping A with the incorrect C, this option confuses fundamental concepts.

Option C:
Option C considers B and C wrong, but B is a correct interpretation of a small p-value in relation to the null hypothesis. Including B among wrong statements therefore invalidates this choice.

Option D:
Option D correctly isolates C as the only wrong statement and leaves all other statements intact as true. It distinguishes between statistical and practical significance, emphasising that significance alone does not ensure usefulness. Hence this combination is the correct answer.

Type II error, also known as beta error, occurs when a statistical test fails to reject the null hypothesis even though it is actually false in the population. In practical terms, this means the test has missed a real effect or difference that truly exists. The probability of committing a Type II error is related to factors such as sample size, effect size and chosen significance level. Because the stem describes failing to reject a false null hypothesis, it is defining Type II error.

Option A:
Type I error occurs when a true null hypothesis is incorrectly rejected, leading to a false positive conclusion. It is controlled by the significance level alpha and is conceptually different from failing to detect a real effect. Since the stem refers to failing to reject a false null, Type I error is not appropriate.

Option B:
The power of a test is the probability of correctly rejecting a false null hypothesis and is equal to 1 minus the probability of Type II error. While related, power is a desirable property rather than an error itself. Therefore, power of the test does not match the error described in the question.

Option C:
Type II error reflects the situation where the test is not sensitive enough to detect the existing effect, perhaps due to small sample size or high variability. Researchers strive to keep this error reasonably low by designing adequately powered studies. Because the stem explicitly mentions failing to reject a false null hypothesis, Type II error is the correct completion.

Option D:
Effect size measures the magnitude of a difference or relationship, independent of sample size, and indicates practical significance. It is not an error probability and does not describe the decision-making mistake outlined in the stem. Consequently, effect size cannot be the right answer here.

The power of a statistical test is the probability that it will detect an effect and reject the null hypothesis when the null is actually false. High power means a lower chance of committing a Type II error, or false negative. Power is influenced by factors such as sample size, effect size, variability in the data and the chosen significance level. Because the stem describes the probability of correctly rejecting a false null hypothesis, it is defining the power of the test.

Option A:
Significance level, often denoted by alpha, is the probability of committing a Type I error—that is, rejecting a true null hypothesis. It sets the threshold for how much risk of a false positive the researcher is willing to accept. Since the stem is about correctly rejecting a false null, significance level is not the correct answer.

Option B:
Effect size quantifies the magnitude of a relationship or difference, providing information about practical importance rather than decision error probabilities. While larger effect sizes generally increase power, effect size itself is not the probability of correct rejection. Thus, effect size does not fit the description in the question.

Option C:
Confidence level refers to the proportion of times that confidence intervals constructed from repeated samples would contain the true population parameter. It is related to estimation rather than hypothesis testing power. Therefore, confidence level is not the term that describes the probability of correctly rejecting a false null hypothesis.

Option D:
Power reflects the sensitivity of a test to detect real effects, guiding researchers in planning sample sizes to avoid underpowered studies that may miss meaningful results. Given that the stem explicitly refers to correctly rejecting a false null hypothesis, power is the appropriate completion.

Explanatory research goes beyond description to investigate why and how certain phenomena occur by testing specific hypotheses about causal relationships among variables. It often uses more rigorous designs such as experiments or sophisticated statistical models to rule out alternative explanations. The goal is to build and confirm theories that explain observed patterns. Because the stem refers to research designed to test hypotheses about cause–effect relationships, explanatory research is the correct term.

Option A:
Exploratory research is conducted when the problem is not clearly defined and the researcher seeks to gain familiarity, generate ideas or identify variables, without strong prior hypotheses about cause and effect. It is thus less focused on hypothesis testing than explanatory research, so it does not fit the stem.

Option B:
Descriptive research aims to present an accurate picture of characteristics, frequencies or distributions of variables, answering “what” questions rather than “why.” It does not primarily test causal hypotheses, so descriptive research is not the right completion here.

Option C:
Explanatory research typically builds on earlier exploratory and descriptive work and requires careful control or statistical adjustment for confounding variables. Its emphasis on causal reasoning and hypothesis testing aligns exactly with the description given in the question.

Option D:
Diagnostic research is oriented toward identifying reasons for specific conditions or problems in applied contexts, such as why a particular programme is failing, but the term is less commonly used as a general label for testing causal hypotheses across variables. Therefore, diagnostic research is not the best answer.

A Type II error occurs when the null hypothesis is actually false but the statistical test does not detect sufficient evidence to reject it. This results in a false negative conclusion, where a real effect or difference is overlooked. The probability of a Type II error is denoted by beta, and its complement is the power of the test. Thus, the situation described in the stem is correctly named a Type II error.

Option A:
Type II errors are more likely when sample sizes are small, effect sizes are weak or variability in the data is high. Researchers try to control this risk by planning adequate sample sizes and using sensitive measures. Because the stem focuses on failing to reject a false null, this option is appropriate and correct.

Option B:
Measurement error refers to inaccuracies in how variables are assessed and can contribute to both Type I and Type II errors but does not specifically name the hypothesis decision error.

Option C:
Sampling error arises from studying a subset rather than the entire population and may contribute to uncertainty but is not the particular decision error of missing a true effect.

Option D:
Computational errors involve mistakes in calculation or coding and can lead to any kind of incorrect conclusion; they are not the conceptual category of error defined in statistical theory as Type II.

In this question, statements A, C, E and F correctly describe the scientific method, while B and D are wrong. Statement A is true because systematic observation and logical reasoning are central to scientific inquiry. Statement C correctly identifies hypothesising, testing and verification as typical steps, and statement E is right that objectivity and replicability are crucial. Statement F is also true since scientific principles can be adapted within qualitative frameworks, whereas B is false because empirical evidence is fundamental and D is false as intuition alone cannot constitute scientific research.

Option A:
Option A treats B as the only wrong statement and ignores D. While B is indeed false because the scientific method depends on empirical evidence, D is also incorrect since relying solely on intuition contradicts scientific rigour. By missing one of the wrong statements, this option does not fully answer the question.

Option B:
Option B correctly identifies both B and D as wrong statements and does not misclassify any of the true statements. It recognises that rejecting empirical evidence and relying only on intuition are both incompatible with the scientific method. Because it captures the full set of wrong statements and excludes the correct ones, this option is the right answer.

Option C:
Option C marks only D as wrong and overlooks B. Although D is clearly false, B is equally incorrect because scientific research requires empirical data. Since this combination fails to include all the wrong statements, it cannot be accepted.

Option D:
Option D wrongly includes statement C among the wrong statements along with B and D. Statement C is actually a correct description of typical steps in the scientific method. By misclassifying a correct statement as wrong, this option becomes logically inconsistent.

Statistical power quantifies the sensitivity of a test to identify real effects when they exist. It is mathematically equal to one minus beta, where beta is the probability of a Type II error. High power means that the test is unlikely to miss true differences or relationships. Therefore, the probability described in the stem is called the power of the test.

Option A:
Precision refers to how close repeated estimates are to one another, such as narrow confidence intervals, but it is not the standard term for the probability of detecting true effects.

Option B:
Power is influenced by factors such as sample size, effect size, significance level and variability in the data. Researchers often conduct power analyses while designing studies to ensure that they have a reasonable chance of finding meaningful effects. This focus on correctly rejecting false null hypotheses matches the stem, making this option correct.

Option C:
Reliability concerns the consistency of measurements over time or across items and is a property of instruments, not directly of hypothesis tests.

Option D:
Validity relates to the appropriateness of inferences drawn from test scores or study designs; it does not specifically denote the probability of detecting true effects in statistical testing.

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.