Reading materials | Weekly Q&A | Lecture notes

Key concepts

  • Standard errors
  • Sampling distribution
  • Law of large numbers
  • Bootstrapping and Central Limit Theorem
  • Confidence Intervals and p-values
  • Student’s t-distributions
  • Null and alternative hypotheses
  • One and two-tailed tests
  • Type I and type II error
  • Sample size

Before class

Required readings

  • Meier, K. J., Brudney, J. L., & Bohte, J. (2014). Introduction to Inference. In Applied Statistics for Public and Nonprofit Administration.
  • Meier, K. J., Brudney, J. L., & Bohte, J. (2014). Hypothesis Testing. In Applied Statistics for Public and Nonprofit Administration.
  • Chapters 9 and 10 in: Klein, G., & Dabney, A. (2013). The Cartoon Introduction to Statistics (First edition). Hill and Wang, a Division of Farrar, Straus and Giroux.

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

P-hacking and replication

In class

  • Lecture on key concepts (1.5h)
  • In class practice (1.5h):
    • Paper practice
    • In-class practice
  • Assignment catch-up (if time allows):
    • Qualitative group paper weekly update (15 min).
    • Quantitative Test (15 min).

After class