Hypothesis Testing and Type Errors

Hypothesis Testing and Type Errors
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[1] Explain Type I and Type II errors. Use an example if needed
The Type I error (false positive) occurs when one rejects the null hypothesis while it is true, while a Type II (false negative) occurs when the null hypothesis is not rejected when the alternative hypothesis is true (Rubin, 2013). For instance, in a situation where the null hypothesis (H0) is that a weight loss pill drug is ineffective while the alternative hypothesis (H1) the pill is effective. If there is a conclusion that weight loss pill is effective whine in reality it is not then this is a Type I error. On the contrary, if we claim that the pill is ineffective while it is effective then this is a Type II error.
[2] Explain a one-tailed and two-tailed test. Use an example if needed.
In hypothesis testing, results can be represented using the normal distribution curve that has two tails. The one-tailed test result is only on one side of the curve typically testing if a value is greater or less than another (Babbie, 2013). For instance, testing whether the average weighted of male students is greater than 150 pound. The two-tailed test is on both sides of the curve. The direction of one-tailed test can only be positive or negative, but it is both positive and negative for the two-tailed t-test (Black, 2010). For example, to test whether the average score in a class is 75% is a form of two-tailed test.
[3] Define the following terms in your own words.
Null hypothesis
This is the prediction that there exist no changes, difference or effect between variables. For instance, exercises have no effect on weight loss.
P-value
It is the probability of obtaining a specific effect, when the null hypothesis is true, and it is related to the significance of the results.
Critical value
This is the point used to compare results with a test statistic, for one to either reject or not reject the null hypothesis. The confidence or significance level then needs to be identified to interpret the results using the critical value.
Statistically significant
Results that are statistically significant are probably true as they do not occur because of chance, and significance levels are set to determine whether patterns are to be believed.
[4]A homeowner is getting carpet installed. The installer is charging her for 250 square feet. She thinks this is more than the actual space being carpeted. She asks a second installer to measure the space to confirm her doubt. Write the null hypothesis Ho and the alternative hypothesis Ha.
Null hypothesis- Ho: The actual space=250 sq. ft.
Alternative hypothesis- Ha: The actual space is < 250 sq. ft.
[5] Drug A is the usual treatment for depression in graduate students. Pfizer has a new drug, Drug B, that it thinks may be more effective. You have been hired to design the test program. As part of your project briefing, you decide to explain the logic of statistical testing to the people who are going to be working for you. 
Write the research hypothesis and the null hypothesis.
H0: Both Drug A and Drug B are equally effective.
H1: Drug B is more effective compared to Drug A.
Then construct a table like th...