# Distinguish the difference between Type 1 and Type 2 errors

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) γ (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