Week 5 — Filling In (9/19–23)
Week 5 — Filling In (9/19–23)#
This week introduces one new statistical concept — the hypothesis test — and is otherwise about practice and solidifying concepts. I’m also going to take a step back and give some more context to some of the things we’re talking about.
Our learning outcomes are:
Compute and interpret hypothesis test
Understand how to read and interpret Python errors
Understand how the quantitative techniques we are learning in this class fit in a broader landscape of epistemologies
🧐 Content Overview#
This week has 1h14m of video and 2653 words of assigned readings. This week’s videos are available in a Panopto folder.
Week 5 Quiz is due on Thursday at 8AM.
Assignment 2 is due on Sunday, September 25, 2022 at 11:59 PM.
Midterm A is next week, on September 28.
📓 Assignment 1 Solution#
The Assignment 1 solution is on Piazza.
📃 Course Glossary#
If you haven’t yet, I highly recommend consulting the course glossary. Please post on Piazza if you have suggested additions!
The glossary is also likely to be useful in studying for the exam next week.
📓 Writing Functions#
I’ve used Python functions in a few of my example notebooks. The function notebook talks more about them, how to write them, and how to use them.
🎥 Comparing Distributions#
This video describes how to use Q-Q plots to compare data against a distribution.
🎥 Testing Hypotheses#
Read XKCD #882: Significant.
This is called p-hacking: running tests until we find one that is significant.
This video discusses the t-test in more detail, and the different kinds of t-tests that we can run. It also introduces degrees of freedom.
📓 Tying It Together#
I will be adding a notebook reading here to tie together some Week 4 and 5 material.
In this video, I talk about how the quantitative data science methods we are learning fit into a broader picture of source of knowledge.
🚩 Week 5 Quiz#
The Week 5 quiz is about material through this point. The subsequent videos are to help you better understand and contextualize material.
📓 One Sample Notebook#
The One Sample notebook demonstrates how to compute a one-sample t-test, and draw a Q-Q plot to compare a distribution with normal.
NIST Handbook on quantitative meaures (has info on 1-sample and 2-sample t-tests)
🎥 Python Errors#
This video discusses common Python errors and how to read errors.
🎥 Python Libraries#
🎥 Learning More#
In this video I talk about how I go about expanding my own data science knowledge and techniques, with the goal of giving you ideas for how you can continue learning beyond this class.
There are a few things you can do to keep practicing the material:
Download the SBA data from Week 4’s activity and describe the distributions of more of the variables.
Apply the inference techniques from Week 4 to statistically test the differences you observed in Assignment 1.
📓 More Examples#
Some more examples from my own work (these are not all cleaned up to our checklist standards):
📩 Assignment 2#
Assignment 2 is due on Sunday, September 25, 2022.