A student is interested in whether there is a relationship between gender and major at her college. She randomly sampled some men and women on campus and asked them if their major was part of the natural sciences (NS), social sciences (SS), or humanities (H).
Her results appear in the table below. What would be the expected frequency of women in social sciences based on this table?
major_table.GIF
The expected value of women in social sciences is the product of the total number of women and the total number of social science majors divided by the total number of participants. (22*34)/57 = 13.12
13.12
0.02
Conduct a Chi Square test to determine if there is a relationship between gender and major. What Chi Square value do you get?
major_table.GIF
First calculate the expected value for each cell. Then take the sum of each (expected - observed)^2/expected. Chi Square = 2.2 (All numbers used in this calculation were rounded to 2 decimal places. Your answer might not be exactly the same if you rounded differently.)
2.23
0.06
Although this is not our view, some people think that the correction for continuity should be used when you have a contingency table with
false
only 4 cells total.
true
an expected cell frequency that is below 5.
false
some cells that are a lot larger than other cells.
Some authors think that the correction for continuity should be used whenever an expected cell frequency is below 5, but research in statistics has shown that this practice is not advisable.
Suppose an experimenter asked a group of 60 participants whether they could be scared by a movie. Then the experimenter had the participants watch a scary movie.
After the movie, the experimenter again asked them if they could be scared by a movie. The experimenter's data appear in the table below. Can this experimenter use the Chi Square test to see whether watching the scary movie made more people say that they could be scared by movies?
scared_table.GIF
false
Yes
true
No
No, it would not be appropriate to use a Chi Square test in this example because each subject contributed data to more than one cell.