For this quiz, use the EG DIB evaluation dataset. Note that the variable “total_ely1” contains Year 1 endline test scores. For now, ignore covariates or clustering in your calculations.
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You can use the ttesti command to calculate the p-value:
ttesti 50 10.8 3.4 9.6
The p-value from a two-sided test is 0.016 < 0.05, and so you can reject the null hypothesis that the true population mean is 9.6 at the 5% significance level.
After loading the EG DIB data you can run the command:
ttest total_ely1 == 10
The p-value on a two-sided hypothesis test is ~0.00 < 0.05, and so you can reject the null hypothesis that the Year 1 endline test score average for this sample of students is 10.0 at the 5% significance level. Note that your sample mean is very precisely estimated because the sample size (4,069 students) is very large. However, this estimate fails to account for the fact that sampling took place in two stages: first schools were sampled and then students were sampled. We will see in the Sampling lesson that adjustments for clustering yield much less precise estimates.
1. Suppose that you sample 50 students and get a sample mean of 10.8 and a standard deviation of 3.4. Would you reject the null hypothesis that the true population mean is 9.6, with a two-tailed hypothesis test and a 5% level of significance? Use Stata’s ttesti command.
2. Suppose that the EG dataset is a sample of students from the target population (rather than the population itself). Using this data, test the null hypothesis that the true population mean of the variable “total_ely1” is 10.0. Would you reject the null hypothesis, using a two-tailed hypothesis test and a 5% level of significance? Use Stata’s ttest command.
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