Case Study Pack: Intro Stats

This “starter case study pack” is a tiny mini-course inside the Workbook:

• one dataset,

• one story,

• and a short sequence of scripts you can run and check.

It’s designed for absolute beginners who want a concrete, repeatable workflow:

Run → Inspect → Check.

The story

A teacher pilots a new study strategy and wants to know if it improves exam scores.

Two groups of students take the same final exam:

• control — normal study habits

• treatment — new strategy

We’ll use the same dataset in five short scripts, each building intuition:

1. descriptives

2. simulation (bootstrap)

3. distributions + outliers

4. confidence intervals

5. hypothesis testing by simulation + effect size

What you get

Inside your workbook folder (created by pystatsv1 workbook init):

Dataset

• data/intro_stats_scores.csv

Scripts

• scripts/intro_stats_01_descriptives.py

• scripts/intro_stats_02_simulation.py

• scripts/intro_stats_03_distributions_outliers.py

• scripts/intro_stats_04_confidence_intervals.py

• scripts/intro_stats_05_hypothesis_testing.py

Write-up template

• writeups/intro_stats_interpretation_template.md

Tests

• tests/test_intro_stats_case_study.py

Outputs go to

• outputs/case_studies/intro_stats/

Read the mini-textbook pages (recommended)

If you are new to statistics, read these short pages as you run each script. Each page explains what the script is doing, what to look for in the outputs, and how to talk about the result in plain language.

Intro Stats mini-course

• Intro Stats 1 - Descriptives and group summaries
• Intro Stats 2 - Simulation and uncertainty (bootstrap)
• Intro Stats 3 - Distributions and outliers
• Intro Stats 4 - Confidence intervals
• Intro Stats 5 - Hypothesis testing by simulation and effect size
• Intro Stats - Interpretation write-up template

Run → Inspect → Check

From inside your workbook folder:

# Part 1: descriptives (means/SDs + a quick histogram)
pystatsv1 workbook run intro_stats_01_descriptives

# Part 2: bootstrap simulation for the mean difference
pystatsv1 workbook run intro_stats_02_simulation

# Part 3: distributions + outliers (IQR rule) + plots
pystatsv1 workbook run intro_stats_03_distributions_outliers

# Part 4: 95% confidence intervals (t-based) + plot
pystatsv1 workbook run intro_stats_04_confidence_intervals

# Part 5: permutation test (p-value by simulation) + effect size
pystatsv1 workbook run intro_stats_05_hypothesis_testing

# Check: confirms the dataset shape + the expected effect direction
pystatsv1 workbook check intro_stats

Inspect your outputs

Open the output folder in File Explorer:

explorer outputs/case_studies/intro_stats

Start with these files:

• group_summary.csv (group means and SDs)

• bootstrap_mean_diff.csv + bootstrap_mean_diff.png

• distributions_summary.csv + outliers_iqr.csv + score_distributions.png

• ci_mean_diff_welch_95.csv + ci_group_means_95.png

• permutation_test_summary.csv + permutation_null_distribution.png

• effect_size.csv

Then (optional) write up your interpretation

The pack includes a tiny write-up template. Copy it and fill in the blanks:

cp writeups/intro_stats_interpretation_template.md writeups/intro_stats_writeup.md

Then open writeups/intro_stats_writeup.md in Notepad (or your editor) and answer the questions.

What you should see

• The treatment group should have a higher mean score than the control group.

• The bootstrap distribution of the mean difference should be mostly above 0.

• The permutation test should usually report a small p-value (because the simulated data was generated with a real group difference).

• The effect size (Cohen’s d) should be in the small-to-medium range.

Notes

• If you’re short on time, Parts 1–2 are the minimum “vibe check”.

• The scripts are intentionally simple and readable — open them and explore!