Respuesta :
Answer:
a) FALSE we obtain a NOT significant results after conduct the hypothesis tes.
b) FALSE, the standard error is not associated to a certain % of people included in the study
c) FALSE. if we want to reduce the standard error we need to increase the sample size or data
d) FALSE, always if we have a higher confidence level the confidence interval associated to this level would be wider than for a lower confidence interval
Step-by-step explanation:
Data given and notation n
n represent the random sample taken
[tex]\hat p=0.52[/tex] estimated proportion of U.S. adult Twitter user get at least some news on Twitter
[tex]p_o=0.5[/tex] is the value that we want to test
[tex]\alpha=0.01[/tex] represent the significance level
Confidence=99% or 0.99
z would represent the statistic (variable of interest)
[tex]p_v[/tex] represent the p value (variable of interest)
Concepts and formulas to use
We need to conduct a hypothesis in order to test the claim that more than half U.S. adult twitter users get some news throught Twitter:
Null hypothesis:[tex]p\leq 0.5[/tex]
Alternative hypothesis:[tex]p > 0.5[/tex]
When we conduct a proportion test we need to use the z statistic, and the is given by:
[tex]z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}}[/tex] (1)
The One-Sample Proportion Test is used to assess whether a population proportion [tex]\hat p[/tex] is significantly different from a hypothesized value [tex]p_o[/tex].
Calculate the statistic
The standard error is given:
[tex] SE = \sqrt{\frac{p_o (1-p_o)}{n}}=0.024[/tex]
Since we have all the info requires we can replace in formula (1) like this:
[tex]z=\frac{0.52 -0.5}{0.024}=0.833[/tex]
Statistical decision
It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.
The significance level assumed is [tex]\alpha=0.01[/tex]. The next step would be calculate the p value for this test.
Since is a right tailed test the p value would be:
[tex]p_v =P(z>0.833)=0.2024[/tex]
So the p value obtained was a very high value and using the significance level given [tex]\alpha=0.01[/tex] we have [tex]p_v>\alpha[/tex] so we can conclude that we have enough evidence to FAIL to reject the null hypothesis.
Now let's idendity the statements
a) FALSE we obtain a NOT significant results after conduct the hypothesis tes.
b) FALSE, the standard error is not associated to a certain % of people included in the study
c) FALSE. if we want to reduce the standard error we need to increase the sample size or data
d) FALSE, always if we have a higher confidence level the confidence interval associated to this level would be wider than for a lower confidence interval
