# Overview

```{admonition} No HW
*No HW submissions this week. But please check-out the readings below.*
```

## Weekly Plan: Introduction - building statistical intuitions

```{topic} [Monday Jan 6th](./1.md)
- Introduce course overview, goals, and instructors
- Learn about you and your goals!
```

```{topic} [Tuesday Jan 7th (LAB)](/labs/1/overview.md)
- Getting setup with git, github, and Python
- Python basics
```

```{topic} [Wednesday Jan 8th](2.md)
- Background survey feedback
- What is statistics?
- Two cultures of statistical modeling
- Bias, variance, and ecological validity
```

## Resources
```{topic} Readings
- Poldrack, R.A. (2024). Statistical Thinking for the 21st Century [Chapter 1](https://statsthinking21.github.io/statsthinking21-core-site/introduction.html#what-is-statistical-thinking) 
- Yarkoni, T., & Westfall, J. (2017). Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 12(6), 1100–1122. [PDF](https://paperpile.com/shared/sBYg0skKKRyuOZs_TfKGlSg)
- Jolly, E., & Chang, L. J. (2019). The Flatland fallacy: Moving beyond low-dimensional thinking. Topics in Cognitive Science, 11(2), 433–454. [PDF](https://paperpile.com/shared/s2jMmR9LYTAWt8kWljci6Sg)
- Meehl, P. E. (1967). Theory-Testing in Psychology and Physics: A Methodological Paradox. Philosophy of Science, 34(2), 103–115. [PDF](https://paperpile.com/shared/sR07e_Ma6SQC93EByL_wXqA)
```