Type 1 and Type 2 errors are concepts related to hypothesis testing in statistics. Here’s how they differ:
Type 1 Error (False Positive):
1. Occurs when: A Type 1 error happens when a null hypothesis that is actually true is incorrectly rejected.
2. Meaning: It’s a false positive, indicating that the test incorrectly indicates a significant result when there is no true effect or difference.
3. Probability symbol: Denoted by α (alpha).
4. Concern: Type 1 errors are generally more serious in situations where false positives could have serious consequences, like in medical testing.
Type 2 Error (False Negative):
1. Occurs when: A Type 2 error occurs when a null hypothesis that is actually false is not rejected.
2. Meaning: It’s a false negative, indicating that the test fails to detect a significant result even though there is a true effect or difference.
3. Probability symbol: Denoted by β (beta).
4. Concern: Type 2 errors can be problematic in situations where failing to identify a true effect could lead to missed opportunities or incorrect conclusions.
In summary, Type 1 errors involve mistakenly rejecting a true null hypothesis, while Type 2 errors involve failing to reject a false null hypothesis. The trade-off between these two errors depends on the context and consequences of the decision being made based on the hypothesis test.
[mediator_tech]
Define Discrete variable
Evaluation
1. A Type 1 error occurs when a _______ null hypothesis is wrongly rejected.
(a) true
(b) false
(c) uncertain
2. Type 1 errors are associated with a _______ positive result.
(a) true
(b) false
(c) neutral
3. The probability of making a Type 1 error is denoted by the symbol _______.
(a) θ (theta)
(b) α (alpha)
(c) β (beta)
4. A Type 2 error takes place when a _______ null hypothesis is not rejected.
(a) true
(b) false
(c) uncertain
5. Type 2 errors are linked to a _______ negative result.
(a) true
(b) false
(c) neutral
6. The probability of making a Type 2 error is represented by the symbol _______.
(a) Îł (gamma)
(b) β (beta)
(c) δ (delta)
7. Type 1 errors are more serious when they lead to _______ consequences.
(a) positive
(b) neutral
(c) negative
8. Type 2 errors become problematic when they result in _______ opportunities.
(a) missed
(b) ample
(c) uncertain
9. Type 1 errors are also called _______ positives.
(a) false
(b) true
(c) ambiguous
10. Type 2 errors are also known as _______ negatives.
(a) false
(b) true
(c) neutral
11. The symbol α (alpha) is associated with controlling the _______ error rate.
(a) Type 1
(b) Type 2
(c) both Type 1 and Type 2
12. The symbol β (beta) is linked to the probability of making a _______ error.
(a) Type 1
(b) Type 2
(c) either Type 1 or Type 2
13. Minimizing Type 1 errors can increase the likelihood of _______ 2 errors.
(a) Type
(b) Type 1
(c) Type 2
14. Balancing Type 1 and Type 2 errors depends on the _______ consequences and goals.
(a) neutral
(b) similar
(c) specific
15. The trade-off between Type 1 and Type 2 errors is influenced by the _______ of the decision.
(a) timing
(b) context
(c) accuracy
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