Week 5 Do we live in a simulation?

Basic data simulation for statistical inference and power analysis

Description

We have known ever since science-fiction author Philip K. Dick’s memorable “Metz address” of 1977 that our world is a computer simulation. Of course, like some common-currency theories in the social sciences, this knowledge will never be truly verified. We won’t even attempt to get to the bottom of it in class; instead, we’ll practice some basic methods of computer simulation for statistical inference and for generating data that has some idealised characteristics. Such methods play an increasingly important role in computational statistics and are extremely useful for designing robust data collection and analysis plans. If you make a mistake in the code and end up in an infinite loop, but you’re afraid that stopping the process may cause the known universe to implode, you can watch Dick on YouTube while you wait. If something like this can happen to our data, who says it couldn’t happen to us?

Readings

• ROS: Chapters 5 (pp. 69-76) and 16 (pp. 291-310)
• TSD: TDS makes extensive use of simulation methods for various purposes at different stages of a research project (e.g. from data preparation through statistical inference to sharing results and data openly). A search on a keyword stub “simulat” can point you various sections of interest that are all worth reading.

References

David, F. N. 1955. “Studies in the History of Probability and Statistics i. Dicing and Gaming (a Note on the History of Probability).” Biometrika 42 (1/2): 1–15. https://doi.org/10.2307/2333419.

El-Shagi, Makram, and Alexander Jung. 2015. “Have Minutes Helped Markets to Predict the MPC’s Monetary Policy Decisions?” European Journal of Political Economy 39 (September): 222–34. https://doi.org/10.1016/j.ejpoleco.2015.05.004.

Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and other stories. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781139161879.

Lord, R. D. 1958. “Studies in the History of Probability and Statistics.: VIII. De Morgan and the Statistical Study of Literary Style.” Biometrika 45 (1/2): 282–82. https://doi.org/10.2307/2333072.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Second. CRC Texts in Statistical Science. Boca Raton: Taylor and Francis, CRC Press.

Mulvin, Dylan. 2021. Proxies: The Cultural Work of Standing in. Infrastructures Series. Cambridge, Massachusetts: The MIT Press.

Senn, Stephen. 2003. “A Conversation with John Nelder.” Statistical Science 18 (1): 118–31. https://doi.org/10.1214/ss/1056397489.