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Erebloth Erebloth
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5 years ago
A null hypothesis is stated. The null hypothesis is, There is no difference between 10 mcg and 20 mcg of vitamin D3 in prevention of osteoporosis. What are the implications of this statement, concerning that hypothesis and type I error?
 
  (Select all that apply.)
  a. Rejecting the null hypothesis when it actually is false means that the researcher has made a type I error in concluding that there is a difference between 10 mcg and 20 mcg of vitamin D3 in preventing osteoporosis.
  b. Making the statement is itself a type I error.
  c. Whether the null hypothesis is true or not makes no difference in terms of type I error.
  d. Whether or not the researcher rejects the null hypothesis makes no difference in terms of type I error.
  e. Rejecting the null hypothesis when it actually is false means that the researcher concludes that there is a difference between 10 mcg and 20 mcg of vitamin D3 in preventing osteoporosis, and there is no error.
  f. Rejecting 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, but the researcher thinks there is and has made a type I error.
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KanashaeaKanashaea
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ANS: E, F
Type I error is the probability of rejecting the null hypothesis when it is in fact true. It is also called alpha . In nursing studies, this is usually .05, which equals 5. To test the null hypothesis, the researcher consults a statistics website or book, to discover the decision point or cutoff point, which is the value at which rejecting the null hypothesis would be the wrong decision only 5 of the time. Then the researcher calculates the statistic, based on the data. If the value of the statistic is less than the cutoff point, the null hypothesis standsit is not rejected. If the value of the statistic is more than the cutoff point, the null hypothesis is rejected. This will be the correct decision 95 of the time.
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