statistical hypothesis testing

(2.4 hours to learn)


Statistical hypothesis testing is a method for deciding what conclusions can be drawn from data. A central question is determining whether an outcome is statistically significant, or unlikely to have arisen by chance.


This concept has the prerequisites:


  • Understand what is required of a hypothesis test in the Neyman-Pearson paradigm
  • Know basic terminology, including:
    • null hypothesis and alternative hypothesis
    • test statistic
    • type 1 and type 2 errros
    • the power function of a test
    • the level of a test
    • simple and composite hypotheses
  • Know what is meant by a p-value and how to compute it from the test statistic and its distribution
    • In particular, understand why it doesn't give the probability that a hypothesis is true
  • Understand why statistical significance doesn't imply that a difference is large in magnitude

Core resources (read/watch one of the following)


See also