Schedule

Schedule#

Date

#

Topic(s)

Covered in class

Reading

Unit 1:

Thinking about scale

Week 1

Monday

3/30/26

1

Welcome to STATS 60!

What is statistics all about? (1) putting numbers in context, (2) designing experiments, (3) making predictions. What to expect from the course.

Wednesday

4/1/26

2

Contextualizing numbers and reasoning about scale

Putting numbers in context, ballpark estimates/Fermi problems

Chapter 5 of “Calling Bullshit”

Thursday

4/2/26

-

Discussion

Fermi problem contest

Friday

4/3/26

3

Cost/benefit analysis; Quiz in class

Cost-benefit analysis: how to fight Malaria?

Unit 2:

Exploratory Data Analysis

Week 2

Monday

4/6/26

4

Data visualization

What is data? Common graphical visualizations of data, including pie chart, bar chart, histogram, scatterplot, box diagram, and time series. How to read each.

Chapter 7 of “Calling Bullshit”

Wednesday

4/8/26

5

Fundamental summary statistics part 1: Central tendency;

Means and averages, median.

The shock of the mean

Thursday

4/9/26

-

Discussion

Vibecoding visualizations

Friday

4/10/26

6

Misleading visualizations; Quiz in class

Examples of misleading data visualizations

Week 3

Monday

4/13/26

7

Fundamental summary statistics part 2: Variability

Standard deviation, variance, quantiles

Statistical thinking for the 21st century, Chapter 3

Wednesday

4/15/26

8

When means mislead

Very different data with similar summary statistics—beware! Multi-modal data, skew, outliers, and their effects on summary statistics.

Thursday

4/16/26

-

Discussion

Friday

4/17/26

9

Fundamental summary statistics part 3: Correlation; Quiz in class

Correlation and trends, scatterplots, regression.

Unit 3:

Probability

Week 4

Monday

4/20/26

10

Intro to probability

Modeling uncertain situations with probability. Calculating probability in simple sampling scenarios.

Chapter 1 of “Blitzstein and Hwang”

Wednesday

4/22/26

11

Coincidences

How surprising is a coincidence? The birthday paradox.

Thursday

4/23/26

-

Discussion

Friday

4/24/26

12

Guest lecture: statistics in sports. Quiz in class

Using the expected value to make decisions in sports.

Week 5

Monday

4/27/26

13

Conditioninal probability

Conditioning: incorporating new information. How dramatically it can change probabilities. P[A|B] is not the same as P[B|A]. Indepdendence.

Wednesday

4/29/26

14

Common mistakes in conditional probability

Base rate fallacy, conditioning without context, prosecutor’s fallacy, defense attorney’s fallacy, generalizing from a biased sample

“On the Psychology of Prediction” by Khaneman and Tversky; “Multiple sudden infant deaths – coincidence or beyond coincidence?” “Why are handsom men jerks?”

Thursday

4/30/26

-

Discussion

Friday

5/1/26

15

Bayes rule; Quiz in class

Using conditional probabilities to make predictions and bets.

Unit 4:

Estimates, Hypothesis Testing, and Experiments

Week 6

Monday

5/4/26

16

Hypothesis testing and p-values

Statistical framework for testing, null hypothesis, alternative hypothesis, p-value by calculation and by simulation, type 1 & 2 errors

Statistical thinking for the 21st century, Chapter 10

Wednesday

5/6/26

17

Hypothesis testing and p-values

Permutation test for correlation

Thursday

5/7/26

-

Discussion

Hypothesis testing for facial stereotyping

Friday

5/8/26

18

Multiple testing and p-hacking, Quiz in class

Testing multiple hypotheses, family-wise error rate, p-hacking, Bonferroni correction

Chapter 9 of “Calling Bullshit”

Week 7

Monday

5/11/26

19

Observational Studies and Randomized Experiments

Observational studies vs. randomized control trials, experimental design

Chapter 4 of “Calling Bullshit”

Wednesday

5/13/26

20

Potential Outcomes Model

Analyzing data from randomized controlled trials in the potential outcomes model, p-values via simulation

Thursday

5/14/26

-

Discussion

Analyzing a randomized controlled trial on the effects of sleep deprivation

Friday

5/15/26

21

Conclusions to draw from observational studies; Quiz in class

Week 8

Monday

5/18/26

22

Estimating from an unbiased sample: size matters

Estimating a quantity with a random sample, large sample size improves accuracy of our estimates. Population vs. sample statistics. Standard deviation of the sample mean.

Chapter 6 of “Calling Bullshit”

Wednesday

5/20/26

23

Confidence intervals Quiz in class

The Normal/Bell curve, confidence intervals, 68-95-99 rule

Statistical thinking for the 21st century, Chapter 7

Thursday

5/21/26

-

Discussion

Friday

5/22/26

24

Estimating from a sample: Sampling and selection bias

Uniform samples vs. biased samples. Common sources of selection bias. Tie to conditional probability

~~Monday~~

-

~~MEMORIAL DAY, NO CLASS~~

Unit 5:

Regression and Machine Learning

Week 9

Wednesday

5/27/26

25

Intro to machine learning

Prediction and fitting a model to samples, linear regression as a first example

Statistical thinking for the 21st century, Chapter 5.0-5.4 and Chapter 14

Thursday

5/28/26

-

Discussion

Review session

Friday

5/29/26

26

k-nearest-neighbors

Nearest neighbor model, classification, effect of coverage and selection bias on decisions

Chapter 8 of “Calling Bullshit”

Week 10

Monday

6/1/26

27

Generating text

Next word prediction, Markov text generators

Wednesday

6/5/26

28

Outtro

Finals week

Monday

6/8/26

Final exam IN PERSON