Descriptive and Inferential Statistics

Read these sections and complete the questions at the end of each section. Here, we introduce descriptive statistics using examples and discuss the difference between descriptive and inferential statistics. We also talk about samples and populations, explain how you can identify biased samples, and define differential 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.