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Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
MODELING
RUNNING THE STATS
STATISTICAL ANALYSIS
HYPOTHESIS TESTING
STATISTICAL MODEL
Data are described by mathematical
models with meaningful parameters
Mathematical models are useful
for describing data
Model can grasp the form of the data from only a
few parameter values
Mathematical models are useful
for making inferences
Formal logic of mathematics allows derivation of
specific properties of parametric descriptions that
would not be obvious from the data alone
Why (not) Bayesian?
Why (not) Bayesian?
Thomas Bayes’ publishes his seminal
paper/theorem
1763
Probabilistic inference independently
rediscovered by LaPlace in 1774
1774
The frequentist bandwagon really gets
rolling
• Ronald Fisher developed the maximum likelihood theory
of optimal estimation
• Jerzy Neyman developed confidence intervals and tests
1900s
Computing capabilities catch up to Bayesian
thinking
New algorithms like Markov chain Monte
Carlo and Bayesian methods gain popularity
1989
Null hypothesis significance testing and p-
hacking begin to be challenged
What will you do?
2000s
Why (not) Bayesian?
Wasserstein, R.L., & Lazar, N.A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133.
http://dx.doi.org/10.1080/00031305.2016.1154108.
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?
Why (not) Bayesian?

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Why (not) Bayesian?

  • 4. MODELING RUNNING THE STATS STATISTICAL ANALYSIS HYPOTHESIS TESTING STATISTICAL MODEL
  • 5. Data are described by mathematical models with meaningful parameters Mathematical models are useful for describing data Model can grasp the form of the data from only a few parameter values Mathematical models are useful for making inferences Formal logic of mathematics allows derivation of specific properties of parametric descriptions that would not be obvious from the data alone
  • 8. Thomas Bayes’ publishes his seminal paper/theorem 1763 Probabilistic inference independently rediscovered by LaPlace in 1774 1774 The frequentist bandwagon really gets rolling • Ronald Fisher developed the maximum likelihood theory of optimal estimation • Jerzy Neyman developed confidence intervals and tests 1900s Computing capabilities catch up to Bayesian thinking New algorithms like Markov chain Monte Carlo and Bayesian methods gain popularity 1989 Null hypothesis significance testing and p- hacking begin to be challenged What will you do? 2000s
  • 10. Wasserstein, R.L., & Lazar, N.A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. http://dx.doi.org/10.1080/00031305.2016.1154108.