# Descriptive and Inferential Statistics

## Inferential Statistics

### Questions

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.

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.