The Bayesian Case for the Resurrection
What do the probabilities actually say when you run the numbers yourself?
How to use this guide
This guide is built for a 45-60 minute small-group conversation about "The Bayesian Case for the Resurrection." Open with prayer, read the framing aloud, and use the questions below to surface what people actually think before you walk through the case. Aim for honest engagement over consensus.
Facilitator tips
- Read the lesson before the meeting; you do not need to be an expert, just a guide.
- Resist the urge to fill silence. The best discussions follow long pauses.
- When someone raises an objection you cannot answer, write it down and follow up next week.
- Close with a single takeaway from each member, not a doctrinal summary.
What we're studying
Skeptics often object: "No amount of evidence can overcome the low prior for a miracle." Bayesian analysis lets us test that claim rigorously. By assigning probabilities for each piece of evidence on two hypotheses — Resurrection and No-Resurrection — and multiplying the resulting Bayes factors, we can see for ourselves whether the evidence is strong enough to overcome a low prior. The interactive calculator below lets you pick your own numbers.
The case in brief
Timothy and Lydia McGrew's landmark chapter in The Blackwell Companion to Natural Theology applies Bayesian reasoning to the resurrection. For each minimal fact (empty tomb, group appearances, Paul's conversion, James's conversion, early creed, willingness to die), the likelihood on Resurrection vastly exceeds the likelihood on No-Resurrection. When these Bayes factors multiply, the cumulative factor reaches the order of 10^40 or higher — overwhelming even extraordinarily low priors. You do not need to accept their specific numbers; even with conservative values the conclusion is robust. The calculator on this page lets you test that claim yourself. Enter your prior, adjust each likelihood, and watch the posterior update in real time.
Argument structure
Conclusion: Given the evidence, a rational posterior probability for the resurrection is decisively high, even from a very low prior.
- Bayes' theorem describes how any rational agent should update beliefs on evidence.
- Each minimal fact is much more expected on Resurrection than on No-Resurrection.
- When Bayes factors multiply, they grow quickly.
- The cumulative Bayes factor overwhelms any plausibly-low prior.
What if someone says...
"Bayesian reasoning gives the illusion of precision with subjective numbers."
Any reasoning with uncertain evidence depends on estimates. Bayesianism makes the dependencies explicit and lets us see which inputs would have to change to flip the conclusion.
"You are stacking the deck with favorable likelihoods."
The calculator gives you the dials. Drop P(evidence | R) to 0.5 and raise P(evidence | ~R) to 0.3 across the board and the cumulative Bayes factor still outruns a 1-in-10,000 prior.
"A posterior close to 1 is implausible for any historical claim."
Bayesianism tracks what your beliefs should be given the evidence. If many independent lines all point the same direction, a high posterior is the rational response.
Discussion questions
- What prior would you honestly start with, and why?
- Which of the evidence likelihoods in the calculator would you push back on? By how much?
- At what combined Bayes factor would you consider the resurrection rationally warranted, even from your chosen prior?
- [Small group] Where in your own life does this question feel most pressing?
- [Small group] Who do you know that wrestles with this — and how could you talk with them about it this week?
Going deeper
- "The Argument from Miracles" (Blackwell Companion to Natural Theology)Timothy & Lydia McGrew · 2009 · Bayesian resurrection
- Reasonable FaithWilliam Lane Craig · 2008 (3rd ed.) · Natural theology
- The Resurrection of Jesus: A New Historiographical ApproachMichael Licona · 2010 · Resurrection
- The Case for the Resurrection of JesusGary Habermas & Michael Licona · 2004 · Resurrection