As researcher designs a study to measure the effect on patient ...

[lbcchick]

As researcher designs a study to measure the effect on patient satisfaction of the nurse stating to the patient at least once a day, You're a good person.
 
  The researcher sets the alpha (type I error) for the study at p <.10 because the intervention is free, it needs next to no time to enact, and it is harmless. If the alpha is set at .10, what is the effect on the beta  and on type II error? (Select all that apply.)
  a. Beta  stays the same.
  b. Type II error becomes less likely.
  c. Type II error becomes more likely.
  d. Beta  decreases.
  e. Beta  increases as well.
  f. Type II error stays the same.

Question 2

A null hypothesis is stated. The null hypothesis is, There is no difference between one baby aspirin every day and no baby aspirin at all in prevention of myocardial infarction.
 
  What are the implications of this statement, concerning that hypothesis and type II error? (Select all that apply.)
  a. Accepting the null hypothesis when it actually is true means that the researcher has made a type II error in concluding that there is no difference between 10 mcg and 20 mcg of vitamin D3 in preventing osteoporosis.
  b. Making the statement is itself a type II error.
  c. Whether the null hypothesis is true or not makes no difference in terms of type II error.
  d. Whether or not the researcher rejects the null hypothesis makes no difference in terms of type II error.
  e. Accepting the null hypothesis when it actually is true means that the researcher concludes that there is no difference between 10 mcg and 20 mcg of vitamin D3 in preventing osteoporosis, and there is no error.
  f. Accepting the null hypothesis when it actually is false means that the researcher concludes that there is no difference between 10 mcg and 20 mcg of vitamin D3 in preventing osteoporosis, when there actually IS a difference. The researcher has therefore made a type II error.

[jharrington11]

Answer to Question 1

ANS: B, D
The researcher chooses the probability of making a type I error when setting alpha , and if the researcher sets the probability of making a type I error quite low, perhaps only 1, the probability of making a type II error, , increases. By the same token, if the researcher sets the probability of making a Type I error quite high, perhaps 10, the probability of making a type II error decreases.

Answer to Question 2

ANS: E, F
Type II error is the probability of retaining the null hypothesis when it is in fact false. In nursing research, type II error is usually set at .20. This means that a type II error, failure to detect a difference when it indeed exists, will occur 20 of the time. One minus beta  equals the power of the study. This is the research study's power to detect a difference when it indeed does exist.

[lbcchick]

Correct answers!

[jharrington11]

Great! Please up vote