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Fisher's exact test

Now, you'll work with the Olympics dataset to look at the relative success of the American swimming and athletics teams. Whether each athlete received a medal is coded as True or False in the MedalTF column of athletes. Do a larger proportion of swimming or athletics participants come home with medals? A Fisher exact test is a useful way to compare proportions of samples falling into discrete categories. To test this, you'll need to perform a Fisher exact test on MedalTF in relation to Sport. pandas and plotnine have already been imported as pd and p9.

This is a part of the course

“Performing Experiments in Python”

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Exercise instructions

  • Using the crosstab() function, produce a cross-tabulation of MedalTF against Sport, save the result as table and print() it.
  • Perform a fisher_exact() test on table and print the result.
  • Compare the p-value to the given alpha and print the message.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a table of cross-tabulations
table = pd.crosstab(____)
print(____)

# Perform the Fisher exact test
fisher = ____(____, alternative='two-sided')
____(____)

# Is the result significant?
alpha = 0.05
if ____ < ____
    print("Proportions of medal winners differ significantly")
else:
    print("No significant difference in proportions of medal winners found")
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