Intent-to-treat data analyses is designed

Intent-to-treat data analyses is designed
 
  a) To preserve the random composition of groups
  b) To keep the investigator from changing the hypotheses that will be studied
  c) To evaluate the results with those who completed the pre, treatment, and post
  d) Estimates data that the subject would have had from others in the same treatment condition but who completed treatment

Question 2

The probability of obtaining the exact data we obtained given our hypothesis is known as
 
  a) Likelihood
  b) Variability
  c) Statistical
  d) Effect

Question 3

Error variance can be increased by
 
  a) Researcher sloppiness
  b) Sample size
  c) More power
  d) Smaller alphas

Question 4

If a researcher relaxes the alpha, what occurs?
 
  a) Lower risk of Type II errors
  b) Lower risk of Type I errors
  c) Less significant findings
  d) Greater predictive results

Question 5

Decreasing variability leads to
 
  a) Increased power
  b) Higher significance
  c) Greater effect size
  d) Type I errors

Question 6

A Bayesian data analysis
 
  a) Pits the null hypothesis against the alternative and asks which one is more credible
  b) Uses three hypotheses instead of two
  c) Compares statistical regression against descriptive statistics
  d) Is used only in qualitative research data analyses

Question 7

What is a concern with the current use of statistical tests?
 
  a) They force researchers to make a binary decision.
  b) They often lead to Type II errors.
  c) They place too much importance on the null hypothesis.
  d) They do not take power and effect size into consideration.

Question 8

A statistically significant effect at the p < .00001 level means that
 
  a) One can reject the null hypothesis.
  b) One can reject the null hypothesis and the investigator's hypothesis is strongly supported.
  c) The result is statistically significant and the effect size is large.
  d) The finding is very significant.