Distinguish clearly between type I and type II errors.

Distinguish clearly between type I and type II errors.

Tabular comparison of Type I and Type II errors:

Remember, Type I and Type II errors are inversely related. As you decrease the risk of one type of error, the risk of the other type increases

Type I Error (False Positive):

• Definition: Incorrectly rejecting a true null hypothesis.
• Symbol: α (Alpha).
• Occurrence: Happens when there is no actual effect, but the test wrongly indicates an effect.
• Risk: Controlled by the significance level (α) chosen prior to the test.
• Consequence: May lead to unwarranted conclusions or actions based on an observed effect that doesn’t actually exist.
• Mitigation: To lower Type I error risk, you can choose a smaller significance level (α) and design experiments carefully.

Type II Error (False Negative):

• Definition: Failing to reject a false null hypothesis.
• Symbol: β (Beta).
• Occurrence: Occurs when there is a true effect, but the test fails to detect it.
• Risk: Controlled by the statistical power (1 – β) of the test.
• Consequence: Results in missed opportunities to detect real effects that are present.
• Mitigation: To lower Type II error risk, you can increase sample size, improve the sensitivity of the test, or adjust the experimental design.

Remember, the distinction between these errors is crucial when interpreting the results of hypothesis testing, as they highlight the balance between making unwarranted conclusions and missing true effects

[mediator_tech]

Explain the concept of significance level.

1. Type I error occurs when a true null hypothesis is incorrectly __________. a) rejected b) accepted c) ignored
2. In Type I error, the risk is controlled by adjusting the __________ level. a) significance b) power c) confidence
3. Type II error happens when a false null hypothesis is wrongly __________. a) rejected b) accepted c) modified
4. The consequence of a Type I error can lead to __________ actions. a) accurate b) unwarranted c) reversible
5. Type II error results in missed opportunities to detect __________ effects. a) real b) hypothetical c) trivial
6. The risk of Type II error is managed by increasing __________ size. a) sample b) effect c) population
7. Type I error is symbolized by the Greek letter __________. a) β (Beta) b) γ (Gamma) c) α (Alpha)
8. Adjusting the significance level affects the likelihood of __________ error. a) Type I b) Type II c) both Type I and Type II
9. Type II error can result in failure to respond to a __________ situation. a) false positive b) true negative c) true positive
10. The statistical power of a test is denoted by 1 – __________. a) α (Alpha) b) β (Beta) c) γ (Gamma)
11. Choosing a smaller significance level reduces the risk of __________ error. a) Type I b) Type II c) both Type I and Type II
12. Type I error involves seeing something that isn’t there, also known as a __________. a) false positive b) true negative c) true positive
13. A Type II error implies missing something that is there, which is a __________. a) false negative b) true positive c) false positive
14. The consequence of a Type II error can lead to __________ real effects. a) overestimating b) underestimating c) detecting
15. Balancing Type I and Type II errors is essential for making __________ decisions. a) arbitrary b) informed c) hasty