As a result, the test is significant and we can draw this conclusion that the population mean is less than 20.
A test statistic is a statistic used for testing statistical hypotheses. A test statistic, which is a tally of a data set that can be used to conduct the test and boils the data down to a single figure, is a common way to characterize a hypothesis test.
a) Here, the test statistic is calculated as:
Z= [tex]\frac{p'-p}{\sqrt{(p(1-p)/n} }[/tex]
Z=[tex]\frac{19.4-20}{\sqrt{(20(1-20)/50} }[/tex]
Z= -2.12
Therefore -2.12 is the required test statistic value here.
b) Given that this is a one-tailed test, the following p-value was calculated using standard normal tables: p = P(Z -2.12) = 0.0169
The necessary p-value in this case is therefore 0.0169.
c) The test is significant in this case, and the null hypothesis can be rejected because the p-value is 0.0169 0.05, which is the level of significance. This means that there is enough data to conclude that the mean is less than 20.
d) For a significance level of 0.05, we have the following data from normal standard tables:
P(Z < -1.645) = 0.05
Therefore, the following is the rejection criteria in this case: Reject H0 if Z -1.645.
We can reject the null hypothesis here and draw the same conclusion as in the previous section because the test statistic value is -2.12 -1.645. As a result, the test is significant and we can draw this conclusion that the population mean is less than 20.
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