Let μ1 : Population mean score of students this year and
μ2 : Population mean score of students last year.
Similarly,
Let "\\bar{x_{1}}" : Sample mean score of students this year and
"\\bar{x_{2}}" : Sample mean score of students last year.
Since we are asked whether the professor can reject the null hypothesis that the average score improved - H0 : μ1≥μ2 against the alternate hypothesis H1: μ1<μ2
Thus, we use a two-sample Z test for population means.
Under H0, the test statistic is -
"Z_{calc} = \\frac{\\bar{x_{1}} - \\bar{x_{2}}}{\\sqrt{(\\frac{\\sigma_{1}^2}{n_{1}}+\\frac{\\sigma_{2}^2}{n_{2}})}} \\sim N(0,1)"
n1 and n2 are the corresponding sample sizes.
We have,
"\\bar{x_{1}} = 568, \\bar{x_{2}} = 562, {n_{1}} =100, {n_{2}} = 125, \\sigma_{1}^2 = 42^2, \\sigma_{2}^2 = 40^2,\\bar x1\u200b\u200b=568,\\bar x2\u200b\u200b=562,n1\u200b=100,n2\u200b=125"
Now,
"Z_{calc} = \\frac{568 - 562}{\\sqrt{(\\frac{42^2}{100}+\\frac{40^2}{125})}} = 1.087499"
We have to test the claim at level of significance of 5% (Thus alpha = 0.05). Since its a left-tailed test, the critical value from standard normal tables is:
"Z_{1-\\alpha} = Z_{0.95} = -1.6449"
Test Criteria: Reject H0 if Zcalc < Z1-alpha (i.e. -1.6449)
Conclusion: Since our Zcalc = 1.09 is greater than -1.6449 we do not have sufficient evidence to reject H0 at 5% level of significance.
Hence, the professor cannot reject the null hypothesis that the average scores improved.
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