# Week 6: Inferential statistics I Hypothesis testing

# Key concepts

- Standard errors (Meier et al., 2015)
- Sampling distribution (Gordon, 2012)
- Law of large numbers
- Bootstrapping and Central Limit Theorem (“Introduction to Bootstrapping in Statistics with an Example,” 2018; Gordon, 2012)
- Confidence Intervals and p-values (“Introduction to Bootstrapping in Statistics with an Example,” 2018; Gordon, 2012)
- Student’s t-distributions (Meier et al., 2015)
- Null and alternative hypoteses (Meier et al., 2015; Gordon, 2012)
- One and two-tailed tests (Meier et al., 2015; Gordon, 2012)
- Type I and type II error (Meier et al., 2015)
- Sample size (Meier et al., 2015)

# Before class

## Required readings

- Meier, K. J., Brudney, J. L., & Bohte, J. (2015).
*Applied Statistics for Public and Nonprofit Administration*(Ninth edition). Cengage Learning., Chapters 10 & 11 - , Chapter 6. Section 6.2-6.4
- Introduction to Bootstrapping in Statistics with an Example. (2018). In
*Statistics By Jim*.

## Recommended readings

- Klein, G., & Dabney, A. (2013).
*The Cartoon Introduction to Statistics*(First edition). Hill and Wang, a Division of Farrar, Straus and Giroux., Chapters 8, 9 & 10 - Moore, D. S., McCabe, G. P., & Craig, B. A. (2014).
*Introduction to the Practice of Statistics*(Eighth edition/Student edition). W.H. Freeman and Company, a Macmillan Higher Education Company., Chapter 6.3 & 6.4 - Wonnacott, T. H., & Wonnacott, R. J. (1990).
*Introductory Statistics*(5th ed). Wiley., Chapters 8, 9.1-9.4 & 9.6 - The New York Times.
*Bunnies, Dragons and the ’Normal’ World: Central Limit Theorem | The New York Times*.

### Bunnies, Dragons and the ‘Normal’ World: Central Limit Theorem

### P-hacking and replication

# In class

Take the pre-lecture survey here.