Descriptive and Inferential Statistics

Inferential Statistics


Question 1 out of 8.
Our data come from _______, but we really care most about ______.

  • theories; mathematical models
  • samples; populations
  • populations; samples
  • subjective methods; objective methods

Question 2 out of 8.
A random sample

  • is more likely to be representative of the population than any other kind of sample.
  • is always representative of the population.
  • allows you to directly calculate the parameters of the population.
  • all of the above are true.
  • all of the above are false.


Question 3 out of 8.
When participants who arrive for a research study are put into treatment groups on the basis of chance,

  • random sampling has occurred.
  • random assignment has occurred.
  • the statistical conclusions will also be absolutely correct.
  • the research findings will be compromised because you should never randomly assign to groups.


Question 4 out of 8.
Uncertainty regarding conclusions about a population can be eliminated if you

a. use a large random sample.
b. obtain data from all members of the population.
c. depend upon the t-distribution.
d. both a and b.


Question 5 out of 8.
Which of the following is (are) true? Using a random sample

  • is to accept some uncertainty about the conclusions.
  • enables you to calculate statistics.
  • is to risk drawing the wrong conclusions about the population.
  • biases your results.


Question 6 out of 8.
A random sample is one

  • that is haphazard.
  • that is unplanned.
  • in which every sample of a particular size has an equal probability of being selected.
  • that ensures that there will be no uncertainty in the conclusions.

Question 7 out of 8.
Which of the following is a random sample of a college student body?

  • Every fifth person coming out of the Campus Center between 8:30am and 10:00am.
  • Lisa Meyer, Todd Jones, and Maria Rivera, whose ID numbers were picked from a table of random numbers.
  • Every 20th person in the student directory.
  • All are examples of random samples.

Question 8 out of 8.

A biased sample is one that

  • is too small.
  • will always lead to a wrong conclusion.
  • will likely have certain groups from the population over-represented or under-represented due only to chance factors.
  • will likely have groups from the population over-represented or under-represented due to systematic sampling factors.
  • is always a good and useful sample.