Answer:
Null hypothesis:
Alternative hypothesis:
Since is a left tailed test the p value would be:
So the p value obtained was a very high value and using the significance level assumed 
we have 
so we can conclude that we have enough evidence to FAIL to reject the null hypothesis.
Be Careful with the system of hypothesis!
If we conduct the test with the following hypothesis:
Null hypothesis:
Alternative hypothesis:
So the p value obtained was a very low value and using the significance level assumed we have 
so we can conclude that we have enough evidence to reject the null hypothesis.
Step-by-step explanation:
1) Data given and notation
n=110 represent the random sample taken
X=97 represent the students who completed the modules before class
estimated proportion of students who completed the modules before class
is the value that we want to test
represent the significance level
z would represent the statistic (variable of interest)
![p_v{/tex} represent the p value (variable of interest) 2) Concepts and formulas to use We need to conduct a hypothesis in order to test the claim that the true proportion is less than 0.8 or 80%: Null hypothesis:[tex]p\geq 0.8](https://tex.z-dn.net/?f=p_v%7B%2Ftex%7D%20represent%20the%20p%20value%20%28variable%20of%20interest%29%20%20%3C%2Fp%3E%3Cp%3E%3Cstrong%3E2%29%20Concepts%20and%20formulas%20to%20use%20%20%3C%2Fstrong%3E%3C%2Fp%3E%3Cp%3EWe%20need%20to%20conduct%20a%20hypothesis%20in%20order%20to%20test%20the%20claim%20that%20the%20true%20proportion%20is%20less%20than%200.8%20or%2080%25%3A%20%20%3C%2Fp%3E%3Cp%3ENull%20hypothesis%3A%5Btex%5Dp%5Cgeq%200.8)
Alternative hypothesis:
When we conduct a proportion test we need to use the z statistic, and the is given by:
(1)
The One-Sample Proportion Test is used to assess whether a population proportion 
is significantly different from a hypothesized value 
.
3) Calculate the statistic
Since we have all the info requires we can replace in formula (1) like this:
4) 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 . The next step would be calculate the p value for this test.
Since is a left tailed test the p value would be:
So the p value obtained was a very high value and using the significance level assumed we have so we can conclude that we have enough evidence to FAIL to reject the null hypothesis.
Be Careful with the system of hypothesis!
If we conduct the test with the following hypothesis:
Null hypothesis:
Alternative hypothesis:
So the p value obtained was a very low value and using the significance level assumed we have so we can conclude that we have enough evidence to reject the null hypothesis.
