Psychological Science & Statistics – From Inquiry to Insight
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This mini-book is **Track B** in the PyStatsV1 documentation. It is written
for undergraduate psychology students (and instructors) who want to connect:

- core ideas from research methods and statistics,
- with **real, reproducible analyses in Python**, and
- with examples that look like actual psychology studies.

What this mini-book assumes
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- You have seen basic ideas like variables, hypotheses, and p-values.
- You may have used SPSS, JASP, or jamovi before.
- You are either new to Python, or you have only used it a little.

You do *not* need to be a math expert. We will emphasize:

- research questions and study design,
- good measurement and data collection,
- clear, honest interpretation of results.

How this track is organized
---------------------------

The chapters are grouped into broad parts:

- **Foundations of psychological science**:
  why we do experiments, threats to validity, and ethical research.

- **Describing and exploring data**:
  distributions, visualization, and effect sizes that matter in psychology.

- **Comparing groups**:
  one-sample, independent, and paired-samples t tests, plus power and planning.

- **Experiments and ANOVA**:
  one-way and factorial designs, interactions, and planned contrasts.

- **Association and prediction**:
  correlation, simple and multiple regression, and mediation-style thinking.

- **Advanced designs and capstone projects**:
  mixed models, repeated measures, nonparametric tests, and full study write-ups.

PyStatsV1 labs
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Most chapters include a **PyStatsV1 lab**:

- a small, psychology-themed dataset (e.g., reaction times, mood ratings,
  memory scores, or intervention effects),
- Python code that reproduces the main analyses,
- and guidance on how to interpret the output as a researcher.

These labs live in the main PyStatsV1 repository so that you can:

- run them locally in a notebook or Python script,
- modify them for your own course or project,
- and use them as templates for your own studies.

How to use this track
---------------------

If you are a **student**:

- Read the conceptual sections first.
- Then open the matching PyStatsV1 lab and run the code yourself.
- Try changing small pieces (sample size, effect size, model specification)
  and see how the results change.

If you are an **instructor or TA**:

- Treat each chapter as a lecture + lab pairing.
- Use the PyStatsV1 scripts as live demos or homework templates.
- Mix and match chapters with your existing syllabus.

Where to go next
----------------

As we build out this track, new chapters will appear under:

- :ref:`Track B – Psychological Science & Statistics (Psych track) <psych-track>`

on the left-hand sidebar.

For more detailed regression theory and diagnostics, you can also follow
**Track A – Applied Statistics with Python (Regression)**, which mirrors a
classic applied regression course in a language-agnostic way (R ↔ Python).