Respuesta :
At a 0.01 significance level, we do not have enough evidence to support the claim that 72% do not fail in the first 1000 hrs of their use.
What is the null hypothesis in statistics?
The null hypothesis in inferential statistics states that two possibilities are the same. The null hypothesis states that the observed difference is solely due to chance. The likelihood that the null hypothesis is true can be calculated using statistical tests.
Sample size, n =1500
The sample proportion of the chips that do not fail in the first 1000 hrs of their use, [tex]\hat{p} = 0.69[/tex]
Null Hypothesis, H0: p=0.72
Alternate Hypothesis, [tex]Ha: p \neq 0.72[/tex]
Test statistic,
[tex]z= \frac{\hat{p}-p}{\sqrt{\frac{p(1-p)}{n}}} - \frac{0.69-0.72}{\sqrt{\frac{0.72(1-0.72)}{1500}}} = -2.5877[/tex]
Critical Value,
[tex]Z_{0.05/2} =[/tex]±1.96
Now, since the absolute value of the test statistic i.e, 2.5877 is greater than the absolute value of the critical value i.e, .1.96, thus we will reject the null hypothesis.
Thus at a 0.01 significance level, we do not have enough evidence to support the claim that 72% do not fail in the first 1000 hrs of their use.
To learn more about null hypotheses, visit:
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