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📚 Statistical Inference for Everyone
Chapters ▾
1 Introduction to Probability2 Applications of Probability3 Random Sequences and Visualization4 Introduction to Model Comparison5 Applications of Model Comparison6 Introduction to Parameter Estimation7 Priors, Likelihoods, and Posteriors8 Common Statistical Significance Tests9 Applications of Parameter Estimation and Inference10 Multi-parameter Models11 Introduction to MCMC12 Concluding Thoughts13 Appendix A: Computational Analysis14 Appendix B: Notation and Standards15 Appendix C: Common Distributions and Their Properties16 Appendix D: Tables

Chapter 8: Common Statistical Significance Tests

The basic idea of common statistical tests in the approach we have taken has been the following:

  1. Observe some data
  2. Construct a model of the data, with a parameter that needs to be estimated, such as the “true” single value (μ, in Section 7.3), or the proportion of the event (θ, in Section 7.4).
  3. Calculate the final, posterior probability of that parameter
  4. “Test” to see if there is a significant (usually 95%) probability that the parameter is not zero.

Adapted from Statistical Inference for Everyone, by Brian Blais (Bryant University), licensed under CC BY-SA 4.0 (dual-licensed under the GNU FDL 1.2 or later; this adaptation uses the CC BY-SA grant). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.