4. The state of Georgia is considering spending $350 million on a computerized mathematics curriculum that for grades 3 – 10. They pilot the program with 250 students in grades 5 and 6 whose end-of-course test scores are compared to the state average score of 150 to see whether the program has been effective. If we are testing Ho:H = 150 vs HaiH> 150, State the Type I error and Type II error in context of the problem. Туре I error: Туре I error:
4. The state of Georgia is considering spending $350 million on a computerized mathematics curriculum that for grades 3 – 10. They pilot the program with 250 students in grades 5 and 6 whose end-of-course test scores are compared to the state average score of 150 to see whether the program has been effective. If we are testing Ho:H = 150 vs HaiH> 150, State the Type I error and Type II error in context of the problem. Туре I error: Туре I error:
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
Transcribed Image Text:### Statistical Analysis of a Computerized Mathematics Curriculum in Georgia
The state of Georgia is considering spending $350 million on a computerized mathematics curriculum for grades 3 through 10. To evaluate the program’s effectiveness, they piloted the program with 250 students in grades 5 and 6. The end-of-course test scores of these students were compared to the state average score of 150.
---
**Hypotheses:**
- Null Hypothesis (H₀): μ = 150
- Alternative Hypothesis (Hₐ): μ > 150
### Understanding Type I and Type II Errors
**Type I Error (False Positive):**
Occurs when the null hypothesis (H₀) is true, but we incorrectly reject it. In this context, a Type I error would mean that the pilot program for the computerized mathematics curriculum is deemed effective (i.e., the mean score is higher than 150) when, in reality, it is not.
**Type II Error (False Negative):**
Occurs when the null hypothesis (H₀) is false, but we fail to reject it. In this context, a Type II error would mean that the pilot program is not deemed effective (i.e., the mean score is not higher than 150) when, in fact, it is effective.
### Contextual Implications:
Understanding errors in hypothesis testing is crucial for educational policy decisions:
- A **Type I error** could lead to the unnecessary expenditure of $350 million on an ineffective program.
- A **Type II error** might result in the dismissal of a potentially beneficial educational tool.
These errors provide insight into the risks associated with making incorrect conclusions based on statistical tests and the importance of choosing appropriate significance levels.
---
By comprehending Type I and Type II errors within this framework, educators and policymakers can make more informed decisions regarding the implementation of new educational technologies and programs.
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