UGC NET Questions (Paper – 1)

The null hypothesis, usually denoted H0, asserts that any observed difference or relationship in sample data is due to chance alone and that there is no real effect in the population. It provides a baseline assumption that statistical tests attempt to reject. Decisions about significance are made by comparing test statistics to critical values or p-levels relative to this hypothesis. Therefore, a hypothesis of no significant difference or relationship is correctly called the null hypothesis.

Option A:
The null hypothesis is central to inferential statistics because rejecting it suggests that the data provide sufficient evidence for a real effect, while failure to reject it indicates that evidence is insufficient. This framing of “no effect” or “no difference” matches the description in the stem, making this option correct.

Option B:
The alternative hypothesis proposes that a real difference or relationship does exist and is accepted only if the null is rejected; it does not state “no difference.”

Option C:
A directional hypothesis specifies not only that a difference exists but also the direction of that difference, such as one mean being greater than another, which goes beyond the neutral position of the null.

Option D:
A causal hypothesis posits cause–effect relationships and may be directional; it does not represent the assumption of no relationship that underlies significance testing.

The alternative hypothesis represents the researcher’s claim that there is a real effect, difference or relationship in the population. It is accepted when sufficient evidence leads to rejection of the null hypothesis. The alternative may be directional or non-directional, depending on whether the direction of the effect is specified. Hence, the competing claim that there is an effect is correctly termed the alternative hypothesis.

Option A:
A research hypothesis is a broader term for the investigator’s expectation and often overlaps with the alternative hypothesis, but in statistical notation the specific competing claim is denoted as the alternative. The stem uses language drawn from statistical testing, so research is less precise here.

Option B:
Option B, alternative hypothesis, is symbolised as H1 or Ha and directly contrasts with the null hypothesis H0. When test statistics fall in the critical region, the decision is to reject H0 in favour of this alternative, which aligns exactly with the question.

Option C:
A directional hypothesis specifies the expected direction of the effect, such as “greater than”, but not every alternative is directional; some are non-directional. The stem does not restrict itself to direction, so directional is not the best answer.

Option D:
A non-directional hypothesis states that a difference exists but does not specify the direction. It is a type of alternative, but the question is about the general competing claim, making the broader term “alternative” more accurate.

A directional hypothesis states both the existence and the direction of a predicted difference or relationship, such as claiming that one teaching method will produce higher achievement than another. Because it specifies which group will perform better, it allows the use of one-tailed significance tests. Such hypotheses arise when theory or prior research provides a clear expectation about the direction of effect. Hence, the hypothesis described in the stem is correctly termed directional.

Option A:
A directional hypothesis narrows the focus of testing by indicating exactly how groups are expected to differ, for example that experimental scores will exceed control scores. This explicit direction has implications for how critical regions are set in statistical tests. The stem clearly refers to predicting which group will score higher, so this option fits precisely.

Option B:
The null hypothesis usually states that there is no significant difference or relationship between groups and does not specify any direction of difference. It serves as a baseline assumption to be tested rather than a directional claim.

Option C:
A non-testable statement cannot be examined through empirical data because it is too vague or abstract, so it does not function as a proper hypothesis in scientific research. This is the opposite of carefully specifying a directional prediction.

Option D:
The term statistical hypothesis is a general label for any hypothesis formulated for testing with statistical procedures and may include both null and alternative forms. It does not necessarily imply direction, so it is too broad for completing the stem.

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 kind of error represents a false positive conclusion, suggesting that an effect or difference exists when it does not. The probability of committing a Type I error is controlled by the chosen significance level, commonly 0.05 or 0.01, denoted by alpha. Because the stem refers to the probability of rejecting a true null hypothesis and mentions alpha, it is defining Type I error.

Option A:
Power of the test is the probability of correctly rejecting a false null hypothesis, representing sensitivity to detect true effects. It is related to factors such as sample size, effect size and significance level but is conceptually different from the error of rejecting a true null. Since the stem is about the probability of rejecting a true null hypothesis, power is not the correct answer.

Option B:
Effect size measures the magnitude of a relationship or difference, independent of sample size, providing information about practical significance. It is not itself a probability and does not describe the likelihood of incorrect decisions about hypotheses. Therefore, effect size does not match the definition given in the stem.

Option C:
Confidence interval is a range of values within which the true population parameter is expected to lie with a specified probability, such as 95%. It is a descriptive index of estimation precision, not the probability of a particular hypothesis-testing error. Consequently, confidence interval is not the right term for the concept described.

Option D:
Type I error is central to hypothesis testing, as researchers choose a maximum acceptable probability of making this error when setting the significance level. Lowering alpha reduces the risk of Type I error but may increase the risk of Type II error, highlighting the trade-off in test design. Because the stem explicitly mentions rejecting a true null hypothesis and alpha, Type I error is the appropriate completion.

The null hypothesis serves as a default position stating that any observed difference or association in the sample is due to chance. It posits no real effect, no difference or no relationship at the population level. Statistical tests assess whether the sample data provide enough evidence to reject this assumption. Thus, H0 represents the benchmark against which alternative claims are evaluated.

Option A:
Option A correctly captures the idea that the null expresses absence of effect or relationship. It is framed in a way that can be tested using probability models and sampling distributions. If evidence contradicts H0 beyond a chosen significance level, it may be rejected.

Option B:
Option B suggests that the null is what the researcher wants to prove at any cost, which is incorrect. Researchers often hope to reject the null in favor of their alternative hypothesis, but they must do so based on evidence, not desire.

Option C:
Option C claims that the null cannot be tested, which contradicts its very role in inferential statistics. Hypothesis tests are structured specifically around assessing H0.

Option D:
Option D concerning sample size is unrelated to the content of the null hypothesis. Sample size affects power but is not itself expressed in H0.

The null hypothesis (H0) typically asserts that there is no significant effect, difference or relationship in the population. It serves as the baseline assumption in statistical testing. Researchers try to gather evidence to decide whether this assumption can be rejected. Hence, the hypothesis of no difference is correctly termed the null hypothesis.

Option A:
A research hypothesis expresses the researcher’s expectation about relationships or differences, often in directional language. It is not usually the default statement of “no difference.” Therefore, it is not the best completion here.

Option B:
The alternative hypothesis (H1) states that a real effect or relationship does exist in the population. It is accepted when the null hypothesis is rejected, so it does not claim “no difference” as described in the stem.

Option C:
Option C, null hypothesis, specifically denotes the statement that any observed differences are due to chance and that there is no real effect in the population. It is the focus of significance testing and is rejected only when evidence is strong. This fits the definition in the question, making this option correct.

Option D:
A directional hypothesis predicts not only that a difference exists but also its direction, such as “greater than” or “less than.” It is a kind of research or alternative hypothesis, not the no-difference statement. So it does not answer the stem.

The null hypothesis is a formal statement used in statistical testing that assumes no real effect, no difference or no association exists in the population between the variables under investigation. It serves as the default position that the researcher attempts to challenge with sample data. If the evidence is strong enough, the null hypothesis is rejected in favour of an alternative hypothesis. Because the stem specifies a hypothesis asserting no difference or relationship, it is clearly describing the null hypothesis.

Option A:
A research hypothesis (or alternative hypothesis) expresses the researcher’s expectation that a particular relationship or difference does exist in the population. It typically predicts a specific direction or pattern of effect. Since the stem states that the hypothesis asserts no difference or relationship, research hypothesis is not the correct term.

Option B:
Null hypothesis plays a central role in significance testing, where statistical procedures estimate the probability of obtaining the observed sample results if the null hypothesis were true. A low probability leads to rejection of the null, suggesting that a real effect may exist. The definition in the stem aligns directly with this idea, confirming null hypothesis as the correct answer.

Option C:
A directional hypothesis predicts not only that a difference or relationship exists but also the direction of that effect, such as one group scoring higher than another. It is not the hypothesis of “no difference” but rather one of specific difference. Therefore, directional hypothesis cannot complete the stem properly.

Option D:
Working hypothesis is a tentative assumption that guides early stages of investigation and may or may not be stated in statistical terms. It need not assert the absence of a relationship; instead, it often suggests a possible effect to be explored. As a result, working hypothesis does not match the definition given in the question.

The null hypothesis is a formal statement asserting that there is no effect, no difference or no relationship between variables in the population. It serves as the default assumption that researchers attempt to reject using sample data and statistical tests. If the evidence is strong enough, the null hypothesis is rejected in favour of an alternative explanation. Because the stem describes a statement of no significant difference or relationship, it clearly refers to the null hypothesis.

Option A:
A research or alternative hypothesis proposes that there is a meaningful effect, difference or relationship in the population. It is usually what the researcher hopes to support by rejecting the null hypothesis. Since the stem emphasises no difference or relationship, research hypothesis is opposite in meaning and therefore not correct.

Option B:
The null hypothesis states that any observed difference in the sample is due to chance and not to a real effect in the population. Statistical tests compute the probability of obtaining the observed data assuming this null hypothesis is true. When this probability is very low, the null hypothesis is rejected, which fits the description in the stem.

Option C:
A working hypothesis is a tentative assumption used to guide initial investigation and may or may not be formally tested statistically. It is a broader idea and does not necessarily state that there is no difference or relationship. Thus, working hypothesis is not the best completion for the definition given in the question.

Option D:
A directional hypothesis predicts not only that a difference exists but also the direction, such as which group will score higher. It is thus a type of research or alternative hypothesis, not the statement of no difference described in the stem. Therefore, directional hypothesis cannot be the correct answer here.