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.