Editing Human Centered Data Science (Fall 2019)/Assignments (section)

==== Writeup: reflections and implications ====
Write a few paragraphs, either in the README or at the end of the notebook, reflecting on what you have learned, what you found, what (if anything) surprised you about your findings, and/or what theories you have about why any biases might exist (if you find they exist). You can also include any questions this assignment raised for you about bias, Wikipedia, or machine learning. 

In addition to any reflections you want to share about the process of the assignment, please respond (briefly) to '''at least three''' of the questions below:

# What biases did you expect to find in the data (before you started working with it), and why?
# What (potential) sources of bias did you discover in the course of your data processing and analysis?
# What might your results suggest about (English) Wikipedia as a data source?
# What might your results suggest about the internet and global society in general?
# Can you think of a realistic data science research situation where using these data (to train a model, perform a hypothesis-driven research, or make business decisions) might create biased or misleading results, due to the inherent gaps and limitations of the data?
# Can you think of a realistic data science research situation where using these data (to train a model, perform a hypothesis-driven research, or make business decisions) might still be appropriate and useful, despite its inherent limitations and biases?
# How might a researcher supplement or transform this dataset to potentially ''correct for'' the limitations/biases you observed?

This section doesn't need to be particularly long or thorough, but we'll expect you to write at least a couple paragraphs.