Uncertainty and Inference

To build up empirical intuition about data, comparisons, and the mechanics of regression modelling, in previous weeks we have focused on understanding the calculation and interpretation of regression coefficients. These ‘point estimates’ may appear as reliable and certain answers to concrete research (sub)questions, but in fact they are a well-intended guess mired in uncertainty. Can they really tell us something reliable about the phenomenon under study in our population of interest, beyond the sample of data that we used to calculate them? Here’s where it becomes essential to develop our understanding of statistical theory that we have so far avoided. Thinking carefully about probability, uncertainty and the challenges of drawing inferences beyond one’s available data will help better understand our – and other researchers’ – results and the claims that can be made on their back.

Readings

Statistics:

• IMS:
  • Chapter 12 (“Confidence intervals with bootstrapping”)
  • Chapter 13 (“Inference with mathematical models”)
  • Chapter 24 (“Inference for linear regression with a single predictor”)
  • Chapter 25 (“Inference for linear regression with multiple predictors”)
  • Chapter 26 (“Inference for logistic regression”)

Further reading:

• IMS: Chapters 16 – 22 (“Statistical Inference” section)

Lecture slides

Download the slides