If we reject the null hypothesis when it is true, we might be making
If we reject the null hypothesis when it is true, we might be making
Solution
The null hypothesis (H₀) represents the "nothing special is happening" statement - what is assumed true unless strong evidence suggests otherwise.
Example: "This coin is fair" (not biased)
The question asks about rejecting the null hypothesis when it is TRUE.
Reality: Null hypothesis IS correct
Decision: Reject it (say it's wrong)
Result: A mistake was made
There are two types of errors in hypothesis testing:
Type-I Error: Null hypothesis is TRUE, but it is REJECTED
Memory trick: "False Alarm" - raising an alarm when nothing is wrong
Type-II Error: Null hypothesis is FALSE, but it is ACCEPTED
Memory trick: "Missed Detection" - missing the actual problem
The question describes: Rejecting null hypothesis when it is TRUE
Null hypothesis is TRUE (reality)
The decision is to REJECT it (wrong decision)
This matches exactly with Type-I Error.
Type-II error occurs when null hypothesis is FALSE but it is ACCEPTED - the opposite situation.
Sampling error relates to differences between sample and population, not hypothesis testing decisions.
Calculation error is a mathematical mistake, not a statistical concept.
Memory aid:
Type-I Error = "Innocent person sent to jail" (person is innocent = null hypothesis is true, but rejected)
Type-II Error = "Guilty person set free" (person is guilty = null hypothesis is false, but accepted)
The answer is Type-I error.
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2026: 21 May Shift 1