STAT 131A: Statistical Methods for Data Science
Instructor: Josh G
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Come to the front to grab a worksheet + hi-chew + say hi!
Starting with Lecture 2, every lecture will start and end with an ungraded conceptual question + attendance check.
You may want to practice finding your seat
and filling out the form! → → →
shorturl.at/rt5m6
Q? pollev.com/jdgg
💻❌ Attendance + tech policy
Lecture attendance is required for 131A.�[ Starting with Lecture 2 ]
No laptops or tablets w/ attached keyboards are allowed during lecture, unless we are coding. Phones are allowed.�[ If you need to use a laptop for accessibility, that's OK! ]
See stat131a.berkeley.edu/fall-2024 for more details.
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Q? pollev.com/jdgg
What will you be able to do after taking 131A? 🤷🏽
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Q? pollev.com/jdgg
🗺️ Naviance
Naviance is an online, proprietary tool designed to guide college search and application decisions.
More than 40% of U.S. high school students have access.
Q? pollev.com/jdgg
📈 The scattergram
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Q? pollev.com/jdgg
📈 The scattergram
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You are here. Should you apply? Why or why not?�[ 🗣️Discuss with neighbor ]
shorturl.at/Hycut
Submit answer here!
Q? pollev.com/jdgg
📈 The scattergram
Suppose your ACT score is below the average score of past students who were admitted.
You may feel dissuaded from applying, even if you are academically qualified to attend.
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Q? pollev.com/jdgg
🤔 Undermatching
Undermatching occurs when a student applies solely to colleges for which they are overqualified.
Extreme example: Perfect GPA + SAT, only community colleges
We find that showing past admissions outcomes [ as scattergrams ] may increase undermatching for strong students.
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Q? pollev.com/jdgg
🔬 Methodology
We filed public records requests on Naviance adoption for 220 public high schools.
We also obtained college application data for 70,000 students from these high schools, spanning 2014–2020.
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Q? pollev.com/jdgg
🏇 Pronounced effect on strong students
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Margin of error (?)
ACT ≥ 29
Q? pollev.com/jdgg
📈 Naviance adoption in Florida
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Q? pollev.com/jdgg
🏫 Aggregated results�In other words, not just Florida!
Access to Naviance appears to approximately double the odds of undermatching among high-achieving students.
�Result is robust to adjustment for potential confounders, such as test scores, GPA, gender, and first gen status.
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?
Q? pollev.com/jdgg
📉 Key takeaways
1. Data visualization choices may have unintended behavioral consequences.
2. After taking 131A, you will have the tools to replicate everything presented so far, and a lot more!
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🍎 STAT 131A Teaching Team
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GSI: Van Hovenga
Instructor: Josh Grossman
Q? pollev.com/jdgg
🥅 Course goals
Learn core statistical concepts and, more generally, learn to reason with data.
Additional goals:�- Build practical statistical intuition�- Become scrappier + more independent.�- Learn R/tidyverse�- Prep for interviews, internships, and full-time jobs�
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Q? pollev.com/jdgg
🥗 Tentative course outline
Week 1: Visualization�Week 2: Data distributions�Week 3: Probability�Week 4: Quantifying uncertainty�Week 5: Confidence intervals�Week 6: Hypothesis testing�Week 7: Midterm 1 (Weeks 1-5) + Linear regression�
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Q? pollev.com/jdgg
🥗 Tentative course outline
Week 8: More linear regression�Week 9: Logistic regression�Week 10: Buffer�Week 11: Non-parametric methods�Week 12: Midterm 2 (Weeks 1-10) + non-parametric methods�Week 13: Decision trees and random forests�Week 14: Buffer�Week 15: More buffer�RRR: Extra help sessions.�Finals period: Final exam
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Q? pollev.com/jdgg
🏢 Course logistics
~6 homework assignments [ 20% of grade ]�Due every other week, roughly.�5 slip days. Can use at most 2 slip days per assignment.
2 midterms + final exam [ 10%+15%+20%=45% of grade ]
Final project [ 15% of grade ]�Tentatively, groups of 3. More details to come.
Labs [ 10% of grade ]
Lecture attendance + participation [ 10% of grade ]
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Q? pollev.com/jdgg
🏢 Course logistics
In general, do not email us. Make a private post on Ed.
Course materials�stat131a.berkeley.edu/fall-2024 + Ed + bcourses
Office hours (OH) + experimental 15-min coffee chats�See website. No office hours during Week 1.��Lab sections�See website. No lab in Week 1. But, try Lab 0 on your own! Post to Ed with questions.
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Q? pollev.com/jdgg
🤖 Large language model (LLM) policy
In 131A, you can only use the PingPong LLM. �[ Unless otherwise indicated. ]
Using any other LLM is considered cheating.
Invites to PingPong coming soon.��See syllabus for full LLM policy.
�Note: PingPong is experimental. Provide feedback! We may adjust the parameters + settings based on feedback.�
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Q? pollev.com/jdgg
📝 To do
1. Read the syllabus!!! Please!!! 😭🙏🏽�2. Student survey�3. Complete Lab 0��If you’d like help assembling a study group, please complete the form on the website by Monday at midnight.
If you have a Letter of Accommodation (LoA), please make a private Ed post ASAP.
This is all on the website: stat131a.berkeley.edu
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Closing concept check
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Starting with Lecture 2, every lecture will start and end with an ungraded conceptual question + attendance check.
For example, I may have asked "What is undermatching?"
You may want to practice finding your seat
and filling out the form! → → →
shorturl.at/rt5m6
Q? pollev.com/jdgg
⚠️ Assessing police discrimination
Assessing discrimination in policing is a critically important but also challenging topic.
It demonstrates both the power and limits of statistical reasoning.
Feel free to participate in the discussion — or take a break from it — to the extent that you are comfortable.
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Q? pollev.com/jdgg
🛑 Stop and Frisk
Officers stop and question pedestrians when there is “reasonable suspicion” of criminal activity.
Until not long ago, 500,000 stops conducted annually in NYC�[ Substantially curtailed at the end of 2013 ]
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Q? pollev.com/jdgg
🛑 Stop and Frisk
If officers suspect that a stopped pedestrian is armed or dangerous, they can conduct a frisk.�[ frisk = a brief pat down of outer clothing ]
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Q? pollev.com/jdgg
🛑 Stop and Frisk
Fact: 80% of stops involved Black or Hispanic individuals.
Fact: 50% of NYC population is Black or Hispanic.
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Q? pollev.com/jdgg
🛑 Stop and Frisk
Fact: 80% of stops involved Black or Hispanic individuals.
Fact: 50% of NYC population is Black or Hispanic.
Is this persuasive evidence of discrimination?
If yes, explain why. �If not, what would be persuasive?�[ Feel free to chat with neighbor ]
� ��
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shorturl.at/6B9nA
Q? pollev.com/jdgg
⚖️ Prima facie evidence
The large raw difference in stop+population proportions is sufficient to initiate a legal claim of discrimination.
On its own, this finding does not prove discrimination.
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Q? pollev.com/jdgg
🛑 Stop and Frisk
“. . . the police are [not] engaged in racial profiling . . . they are stopping people in those communities who fit descriptions of suspects or are engaged in suspicious activity.”��Michael Bloomberg, former New York City Mayor�Washington Post Op-Ed [ 2013 ]
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Q? pollev.com/jdgg
🎛️ Adjusting for observables
Officers report stop data on a UF-250 form.�[ e.g., demographics, location, reason(s) for stop ]
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Q? pollev.com/jdgg
🎛️ Adjusting for observables
Using UF-250 data, we can compare frisk rates for pedestrians who differ only in their recorded race/ethnicity.�[ the same sex, age, stop reason, location, ... ]
Is this an appropriate strategy to test for discrimination?�[ Feel free to chat with neighbor ]
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Answer here: shorturl.at/6B9nA
Q? pollev.com/jdgg
🎛️ Adjusting for observables
What about the factors we do not observe?�[ Omitted-variable bias ] �
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Q? pollev.com/jdgg
🎛️ Adjusting for observables
What about the factors we do not observe?�[ Omitted-variable bias ]
Can we fully trust the data?�[ e.g., UF-250 is filled out after the stop, not before ]�
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Q? pollev.com/jdgg
🎛️ Adjusting for observables
What about the factors we do not observe?�[ Omitted-variable bias ]
Can we fully trust the data?�[ e.g., UF-250 is filled out after the stop, not before ]
Does "differ by only race/ethnicity" even make sense?�[ e.g., location strongly correlated with race+ethnicity ]�
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Q? pollev.com/jdgg
🚪 Outcome tests
Rather than action rates, look at action success rates. �[ An “outcome test” (Becker, 1957) ]
Hit rate�Proportion of frisks that "successfully" recovered a weapon.
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Q? pollev.com/jdgg
🚪 Outcome tests�Hypothetical scenario
Among frisked Black pedestrians, 2% had a weapon.
Among frisked white pedestrians, 4% had a weapon.
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Q? pollev.com/jdgg
🚪 Outcome tests�Hypothetical scenario
Among frisked Black pedestrians, 2% had a weapon.
Among frisked white pedestrians, 4% had a weapon.
How might you interpret this result?�[ Feel free to chat with neighbor ]
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Answer here: shorturl.at/6B9nA
Q? pollev.com/jdgg
🚪 Outcome tests�Hypothetical scenario
Among frisked Black pedestrians, 2% had a weapon.
Among frisked white pedestrians, 4% had a weapon.
On average, frisked white pedestrians were riskier.�[ i.e., twice as likely to have a weapon ]
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Q? pollev.com/jdgg
🚪 Outcome tests�Hypothetical scenario
Among frisked Black pedestrians, 2% had a weapon.
Among frisked white pedestrians, 4% had a weapon.
On average, frisked white pedestrians were riskier.�[ i.e., twice as likely to have a weapon ]
Therefore, they may have been held to a more lenient standard.�[ i.e., only frisked if they appeared extra risky ]
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Q? pollev.com/jdgg
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White
More risky
Less risky
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White
4%
weapon recovery rate
from frisks
Frisked
Not frisked
More risky
Less risky
Q? pollev.com/jdgg
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2%
weapon recovery rate from frisks
White
Black
4%
weapon recovery rate
from frisks
Frisked
Not frisked
Frisked
Not frisked
More risky
Less risky
Q? pollev.com/jdgg
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0%
0%
0%
0%
0%
0%
0%
1%
1%
1%
3%
4%
2%
weapon recovery rate
from frisks
White
Black
0%
0%
0%
0%
0%
1%
2%
2%
3%
5%
4%
weapon recovery rate
from frisks
Frisked
Not frisked
Frisked
Not frisked
Perceived chance of carrying weapon
Q? pollev.com/jdgg