Given :
n1=50, "\\overline{x_{1}}=173.3" , s1=6.4
n2=50 "\\overline{x_{2}}=171.5" ,S2=7.1
Step 1: State the hypothesis
H0: "\\mu _{1}=\\mu _{2}"
H1: "\\mu _{1}> \\mu _{2}"
Step 2:
Name of the test:
since population sd known, use 2 sample t test
Step 3 : Test statstic:
"Z= \\frac{\\overline{x1}-\\overline{x2}}{\\sqrt{\\left ( s1^{2}\/n1-(s2^{2}\/n2) \\right )}}"
"Z= \\frac{173.3-171.5}{\\sqrt{\\left ( 6.4^{2}\/50-(7.1^{2}\/50) \\right )}}"
Z=1.332
Step 4: Since it is observed that t = 1.332 , CV = 1.661
t=1.332≤CV =1.661, and p value = 0.0931 >0.05,
it is then concluded that the null hypothesis is not rejected.
Step 5: It is concluded that the null hypothesis Ho is not rejected. Therefore, there is not enough evidence to claim that the population "\\mu _{1}> \\mu _{2}" , at the 0.05 significance level.
That is, p-values tend to become smaller as sample size increases, unless H0 is true.
Hence when sample size of each data is 80, p values becomes smaller and hence result is significant.
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