You're working as a sports journalist at a major online sports media company, specializing in soccer analysis and reporting. You've been watching both men's and women's international soccer matches for a number of years, and your gut instinct tells you that more goals are scored in women's international football matches than men's. This would make an interesting investigative article that your subscribers are bound to love, but you'll need to perform a valid statistical hypothesis test to be sure!
While scoping this project, you acknowledge that the sport has changed a lot over the years, and performances likely vary a lot depending on the tournament, so you decide to limit the data used in the analysis to only official FIFA World Cup matches (not including qualifiers) since 2002-01-01.
You create two datasets containing the results of every official men's and women's international football match since the 19th century, which you scraped from a reliable online source. This data is stored in two CSV files: women_results.csv and men_results.csv.
The question you are trying to determine the answer to is:
Are more goals scored in women's international soccer matches than men's?
You assume a 10% significance level, and use the following null and alternative hypotheses:
#import packages
import pandas as pd
import matplotlib.pyplot as plt
import pingouin
#Import datasets
women = pd.read_csv('women_results.csv')
men = pd.read_csv('men_results.csv')1. Explanatory Data Analysis
#Viewing women dataset
women.head()women.info()women.describe()#Men dataset
men.head()men.info()men.describe()2. Filter the data
- Require data that's only official FIFA World Cup matches (not including qualifiers) since 2002-01-01
- start with women's data; check tournaments played.
women['tournament'].value_counts()Check the men's data
men['tournament'].nunique()2.1 Filter only FIFA World Cup matches