by: Than Christopoulos / Updated: May 22, 2025 / 7:55pm EST

Editors Note- This is an academic-level version of the more pop level response I wrote.
If you want a more condensed and simpler read, please consider reading that version instead.

Summary

Paulogia, yet again, misunderstands the Max Data case for the resurrection of Jesus. This is an article unpacking the logic, and the issues with the way he and many other skeptics engage with the argument

Paulogia recently tweeted this.

This sounds clever—until you think about it for more than five seconds, because in reality, it reflects a deep misunderstanding of both the methodology and the epistemology behind historical arguments for the resurrection of Jesus. Beneath the tweet’s rhetorical smirk lies a serious confusion—one that collapses careful historical inference into the caricature of blind faith, and dismisses sophisticated evidential models by reducing them to Sunday school slogans.

Now to be clear, this kind of dismissal isn’t surprising from Paul. It’s one of his go-to rhetorical moves. Why offer detailed engagement with a nuanced model when you can say something that sounds smart—especially if you say it with confidence and derision? It’s a pattern I’ve encountered firsthand. See, for instance, how badly he misunderstood—or misrepresented—my position in his video here, and how Mike and I responded here.

So yes, I’m writing a blog post responding to a tweet. And there’s a reason for that.

I regularly get emails and messages from thoughtful people—Christians and skeptics alike—asking how to respond to one-liners like this. People feel the rhetorical weight of these kinds of objections, even when the intellectual content is hollow. That’s because tweets like Paul’s operate as intuition pumps: they bypass argument and appeal directly to audience bias. Add in a bit of well-poisoning—call your interlocutor an “Apologist” (derogatory) so your audience assumes they’re just inventing excuses—and you’ve got yourself a demagogue’s toolbox. You don’t need to make an argument. You just need to make the audience feel like one’s already been made.

The rest of this post unpacks what I think is actually going on behind Paul’s comment—philosophically, epistemically, and historically—especially in light of my past interactions with him. Because once we slow down and examine the assumptions, it becomes clear that the tweet isn’t just wrong. It reveals the very dogmatism and double standard it pretends to critique.

We’ll explore:

What the Historical Reportage Model (HRM) actually is
What the Maximal Data Case involves as a historical inference, not a theological presupposition
Why Paulogia’s objection relies on outdated and poor Humean reasoning
And how some deeper mistakes skeptic’s make when it comes to this topic.

So buckle up. While this is only going to briefly cover a lot of the important points, it is still going to be rather dense… Because once we clear away the rhetoric, what remains is just a mix of bad reasoning.

§1: The Historical Reportage Model (HRM) And The Max Data Case

Honestly, I’m a little confused that Paul is still making this mistake. In the videos I linked above, we explicitly pointed out where he collapsed categories. Apparently, saying this clearly in my debate with Matt Dillahunty wasn’t enough. Neither was the correction Mike and I offered in a livestream.

So let’s clarify what he’s trying to dismiss.

His one-liner only works if he collapses two distinct things into one. But the Historical Reportage Model (HRM) and the Maximal Data Case are two separate stages of reasoning. They are not interchangeable, and neither functions like the other.

The Historical Reportage Model (HRM)

Just like we have “models” of the universe, we can also have “models” of many other things. In this case, the HRM is a hypothesis about the genre and intent of the Gospel documents. It asks:

What kind of literature are we dealing with?
Did the authors intend to report real events in the life of Jesus as accurately as possible, based on eyewitness testimony?

That’s it.
The HRM is not a claim that the Gospels are inspired.
It is not a claim that the Gospels are inerrant.
It is not even a claim that the events described actually happened.

What the HRM affirms is this:

That the Gospel authors understood themselves to be operating within the broad genre of historical biography.
That they sought to record the deeds and teachings of Jesus in good faith, based on sources they considered reliable (including eyewitnesses and early oral tradition)
And that they did not see themselves as having literary license to invent or reshape historical events for purely theological, symbolic, or moral purposes.

To be clear…This is not question-begging—it’s a modeling assumption, used here to explore what the evidence implies if the documents are treated as testimony. For full argumentation on why the HRM is justified, see our series doing just that. If HRM holds, then the Gospels—whatever else one believes about them—function as access points to the earliest public testimony about Jesus. That includes what the original witnesses claimed happened. Again, this does not translate to

“the Gospels say X therefore X”

A critic could accept HRM and still say, “Yes, Luke wrote what he believed was accurate—but his sources were wrong.” That’s a fair counter-hypothesis. What’s not fair is pretending that HRM means “Jesus rose because the Gospels say so.” That’s either a category error—or a straw man.

In other words, HRM doesn't smuggle in the conclusion. It is only speaking on what kind of documents we are dealing with.

Why does this matter? Because the model you choose frames the data you analyze. If HRM is true, then the prior probability of a given pericope being invented is low. If a Skeptical Model is true—one where the authors felt free to fabricate or embellish—the prior probability of a given pericope being made up is high.

Again, this does not mean the events reported necessarily happened. If Luke reports that person A said X happened, and HRM holds, person A’s report of X could still be fabricated, mistaken, or embellished. The HRM simply gets us to the testimony.

So, we separate the historical investigation into two stages:

What kind of document were the Gospel authors trying to write? (HRM)
Given that, what actually happened? (Max Data Case)

The HRM answers only the first. The second depends on further evaluation. From this point forward, all of our evidential assessments assume the HRM is true. That is not question begging. It’s simply conditional reasoning: Given that the Gospels were written with serious historical intent and based on eyewitness claims, what is the probability of this evidence under competing hypotheses? If you want support for the HRM, see this series.

The Max Data Case

The Maximal Data Case builds upon the HRM but remains firmly within the domain of public historical reasoning. It asks:

If the Gospel authors were reporting eyewitness-based claims in good faith—without the freedom to invent historical details—then what best explains the content, pattern, and effects of that testimony, given the cultural, political, and religious context in which it emerged?

This is where explanatory models come in, as well as a proper methodology

The Maximal Data Case looks at historically accessible facts—such as (but not limited to):

The crucifixion of Jesus under Pontius Pilate,
The early proclamation of the resurrection in Jerusalem,
The empty tomb (as independently attested across traditions),
The poly-modal, post-mortem appearances to individuals and groups,
The conversions of skeptics like James and Paul,
And the rise of a resurrection-centric movement that exploded across hostile cultural environments…

…and it asks: “What is the probability of all this data, given the differing hypotheses and our background knowledge?”

In other words, given these data points, the question becomes: Which hypothesis best accounts for them? And this is where a formal structure is not just helpful, but necessary.

In formal terms, we are evaluating a likelihood ratio (don’t worry I will translate the math in section 3) :

Probability of the evidence given the resurrection and background knowledge

Probability of the evidence given any alternative theory and background knowledge

Where:

• E = the body of historical evidence

• HRes = the resurrection hypothesis

• HAlt = any competing hypothesis (e.g., hallucination theory, theft, legend)

• k = relevant background knowledge, including the HRM (i.e., that the Gospels are good-faith historical reportage)

Here is a simple translation of the math. The numerator (the top half) is “the probability of the evidence given the resurrection and our background knowledge” and the denominator (the bottom) is “the probability of the evidence given (any alternative hypothesis to the resurrection) and our background knowledge”

Put simply, we are just asking about the ratio of how expected the data is given any competing hypotheses. It sounds fancy, but this is just how sound reasoning works in any intellectual discipline. If some data is more expected on one hypothesis over another, then that is going to raise the probability of hypothesis.

This matters for three reasons:

HRM is part of the background knowledge (k), not the evidence.
Once granted, we treat the Gospel documents as sincere reportage—not myth, late legend, or symbolic fiction.
The likelihood ratio is separate from prior probability.
Even if one assigns a low prior to the resurrection, the evidence may still be far more expected under that hypothesis than under any alternative.
It gives our methodology structure.
This isn’t “what feels true.” It’s inference to the best explanation based on publicly accessible data.

Importantly, we don’t presume the resurrection. It must earn its place in the model by explaining the evidence better than all alternatives.

Of course, skeptics often appeal to low priors to dismiss the resurrection from the outset. But that’s a different discussion—one we’ll address in Section III (Humean Assumptions) and Section IV (Bayesian Priors and Background Knowledge).

For now, the point is this: the Maximal Data Case isn’t “just believe” with footnotes. It’s a rigorous, explanatory framework.

Why Skeptics Collapse The Two

Paulogia’s rhetorical move only works if he collapses two distinct stages of reasoning into one—and then flattens them into “because the Bible says so.” But why does this conflation happen so often?

There seems to be a few options on the table…one being that its just an honest mistake.

If I am being less charitable, it seems to be because if the Historical Reportage Model (HRM) is granted, the resurrection becomes too evidentially powerful to dismiss without shifting the standards. And that’s a problem for the skeptic.

Let’s be more precise.

If the Gospels are treated as sincere historical reportage based on eyewitness testimony (HRM), then we have access to the original public claims surrounding the resurrection of Jesus. That pushes the debate into the realm of inference—not theological dogma. And from there, the Maximal Data Case begins doing real explanatory work. It says: “Given this data, what hypothesis makes the most sense?”

And here’s the underlying tension: if you’re already committed to the Humean idea that miracle claims are so improbable that no amount of testimony could ever justify belief, then the Maximal Data Case becomes threatening. Because on explanatory grounds alone, the resurrection might actually come out ahead.

That puts the skeptic in an epistemically awkward position. If the data really are more probable on the resurrection hypothesis than on the alternatives, then something has to give—either the prior assumptions, or the integrity of the method.

To avoid facing that dilemma, many skeptics opt to short-circuit the conversation entirely. The move is simple: collapse the HRM and the Maximal Case into one flattened caricature—“You believe in the resurrection because the Bible says so.”

It’s rhetorically efficient. But intellectually evasive.

Not only is this both logically and mathematically mistaken (as we will see later) but it is also a move of epistemic self-protection. By conflating the two stages—literary intent and explanatory inference—the skeptic avoids having to engage either. It's much easier to mock than to methodically evaluate.

So let’s be clear:

The HRM is a claim about literary genre, authorial intention, and proximity to sources.
The Maximal Data Case is a probabilistic inference from public data to the best explanation.

Neither one assumes inspiration or inerrancy. Neither assumes the resurrection actually happened. Neither requires prior belief in a miracle. But once these distinctions are made explicit, the rhetorical power of Paulogia’s tweet dissolves.

Because now the real question is this:

If the Gospel authors were engaged in good-faith historical reportage, and the evidence is what it is—then what best explains what they claimed?

That’s where the real argument begins. And it’s precisely the argument skeptics like Paulogia prefer not to have. Let’s move on.

Hume

Paulogia’s dismissal of the Maximal Data Case as “just believe with extra steps” comes dressed with another assumption that—before even examining the data—miracle claims are disqualified on epistemic grounds. It’s Humean dogma.

And it’s one of the most persistent—and in my opinion intellectually bankrupt—moves in the modern skeptic’s playbook.

What Do Humeans Think?

In An Enquiry Concerning Human Understanding, Hume famously argued that:

“A miracle is a violation of the laws of nature; and as a firm and unalterable experience has established these laws, the proof against a miracle, from the very nature of the fact, is as entire as any argument from experience can possibly be.”

Now to be fair, there are multiple ways to interpret what Hume means here.

Some take him to be making a strong metaphysical claim: that miracles are impossible. But I don’t think Paulogia would want to endorse that stronger reading. So let’s engage with the more modest interpretation, which is more common today among philosophical skeptics.

Under the more charitable reading, Hume isn’t arguing miracles are impossible. Rather, he’s claiming they are so intrinsically improbable—so contrary to uniform human experience—that no amount of testimonial evidence could ever reasonably establish one. Why? Because testimony is always weaker than the “firm and unalterable” course of nature.

That’s the move Paulogia is making here. The Maximal Data Case could be airtight—every line of testimony could align with every independent source—and it wouldn’t matter. Because on this view, miracles are ruled out before the evidence is even considered.

Why This Isn’t Neutral Skepticism

I will offer a more in depth critique of the Humean view later on, but for now I just want to say the following. Let’s be absolutely clear: this isn’t historical caution. It’s not a call for critical thinking. It’s a built-in firewall that shields naturalism from evidential challenge by making miracle claims inadmissible by definition.

The result is a rigged game:

If you argue for a miracle based on testimony, the skeptic says testimony can never overcome the prior improbability.
If you strengthen the testimony, the skeptic insists it’s still probably fabricated or mistaken—because miracles don’t happen.

It’s an epistemic Catch-22. And notably, even non-Christian philosophers have started to move away from it.

In fact, Earman’s evaluation of Hume’s anti-miracle argument is devastating. He writes:

“When these principles [Hume’s] are made explicit and examined under the lens of Bayesianism, they are found to be either vapid, specious, or at variance with actual scientific practice.”
—John Earman, Hume’s Abject Failure, p. 70

He goes on to say that Hume maintained the illusion of a powerful argument…

“by maintaining ambiguities… and by liberal doses of sarcasm and irony”

Sounds familiar doesn’t it?

This isn’t just a weak argument—it’s bad epistemology dressed up in clever rhetoric. As Earman summarizes:

“The essay represents the kind of overreaching that gives philosophy a bad name.”

And that’s exactly the spirit of Paulogia’s tweet. It doesn’t matter what the Gospels claim, what explanatory scope the resurrection hypothesis has, or what methodological caution is employed. The conclusion is disqualified not because of the data, but because of a pre-commitment.

Why Hume (And Paulogia) are Wrong

With all this in mind…Paulogia’s objection isn’t just rhetorically dismissive—it’s also philosophically fragile. Like many modern skeptics, he borrows from a Humean framework that misunderstands probability, abuses priors, and collapses under scrutiny. In this section, we’ll show how a proper Bayesian framework undermines the entire Humean objection—and why the resurrection remains on the table as a live historical hypothesis.

What Is Bayesian Reasoning?

In order to to keep going, we need to talk about probability theory a little bit. If you want to argue that miracles are implausible, you need to speak the language of probability. That’s what David Hume tried to do—and what modern skeptics like Paulogia continue to echo. But the moment you step into that domain, you’re stepping into the world of Bayesian reasoning.

And when used correctly, Bayesian reasoning doesn’t block miracles. It simply asks us to evaluate them in proportion to the evidence.

So let’s walk through what this actually looks like.

The Basic Structure

Bayes’ Theorem gives us a formal way of updating our beliefs when we encounter new evidence. It doesn’t tell us what to believe outright—it tells us how to responsibly adjust our confidence in a hypothesis in light of new data.

The formal version looks like this, I will be using the odds form of the equation because this is going to be about competing theories.

Where..

P(H | E) = Posterior odds that the hypothesis is true, given the evidence

P(¬H | E) = Posterior odds that the alternative hypothesis is true, given the evidence

P(H) / P(¬H) = Prior odds (your initial confidence in H before seeing the evidence)

P(E | H) = Likelihood: how expected the evidence is if H is true

P(E | ¬H) = Likelihood: how expected the evidence is if H is false

What Does All This Mean?

Why use ratios in the first place?

Because we’re comparing two competing hypotheses—one that says the event or claim is true (H), and one that says it’s false (¬H). The goal isn’t to ask, “Is this absolutely certain?” but rather, “Which of these two explanations is better supported by the evidence?”

That’s exactly what the odds form of Bayes’ Theorem is built for. It gives us a way to measure how the evidence shifts the balance between two competing ideas.

What the Equation Does

Let’s break it down:

The left side of the equation tells you your final/updated odds:
How confident should I be in this hypothesis after considering the evidence?
The right side is a product of two ratios:

Prior Odds — your initial confidence in H before seeing the evidence(prior probability)
Likelihood Ratio — a measure of how much more expected the evidence is if H is true versus if it’s false

The likelihood ratio is the crux of the formula. It tells us how much more expected the evidence is if one hypothesis is true compared to the alternative. And this matters—a lot. Even if your prior odds are low, you can still end up with high posterior odds if the evidence is much more expected on one side than the other.

What the Output Means

The output of the equation is an odds ratio. For example:

A result of 5:1 means that, given the evidence, the hypothesis is five times more likely than the alternative.
A result of 1:1 means the evidence doesn’t favor one side over the other.
A result of 1:10 means the hypothesis is ten times less likely than its competitor, based on the evidence.

You can also measure the output in terms of probability rather than odds. For example, take a ratio of 5/1, when done like this the output is 5. For context here, if the output is 1, the product of the prior probability and likelihood ratio is not determinate and each hypothesis is equally probable given the data and background knowledge. The closer to 0, the more likely the denominator is, and the further away from 1 trending positively (ie; from 1 to say 100, or 1000, etc.) the more likely the numerator is.

These odds can be converted to probabilities if needed, but the key idea is this: Bayes helps you weigh competing explanations in light of the evidence—and tells you which one the data leans toward. Here is the formula for converting the odds to probabilities, you’ll see me use this soon.

Here is a simple guide to understanding how odds relate to probability as well.

The Tiger Example

Imagine someone tells you they saw a tiger loose in your suburban neighborhood. You hear someone say:

“There’s a tiger loose in the neighborhood!”

Your initial reaction? No way. Tigers don’t roam the suburbs. So your prior odds—which are a way of expressing how unlikely something is before seeing new evidence—are very low. (For example, odds of 1:9,999 means a 0.01% chance.)

But now let’s break it down using Bayes' Theorem step by step:

Step 1: Define the Hypotheses

H: There is a tiger loose in the neighborhood.
¬H: There is not a tiger loose—something else is going on (a prank, misidentification, etc.)

Step 2: Assign Prior Odds

How likely did you think this was before hearing any evidence?

Let’s say:

P(H) = 1 in 10,000 → the prior probability of a loose tiger
P(¬H) = 9,999 in 10,000 → the prior probability that there isn’t

We compute the prior odds:

Step 3: Evaluate the Evidence

Now suppose:

You text 3 friends and ask them if they heard about the tiger, all of them happened to be in a group and claim to have seen the tiger themselves.
In your background knowledge, you know they are truthful people that would not play a prank on you, and you know they each have jobs in the area where the alleged tiger is loose.

That’s your (the body of evidence).

We now ask:

P(E | H): How likely is it that we’d see this evidence if there really were a tiger loose? Let’s say it’s quite high. Maybe 0.95 (95%).
P(E | ¬H): How likely is it that we’d see this same evidence if there were no tiger? Much lower. Maybe 0.001. We compute the likelihood ratio:

Step 4: Multiply by the Prior Odds

Bayes' odds form:

Now we just fill in the numbers

Step 5: Convert to Posterior Probability

We’ve been using odds so far, since Bayes’ Theorem is clearest in that form. But now we’ll convert them into probabilities so the result is easier to grasp intuitively. Remember our conversion formula is

So….

Or more simply..

Step 6: Interpret the Result

You started with near-total skepticism: just a 0.01% chance that the tiger report was true.

Then you received one compelling piece of evidence—testimony from three trusted friends who were in the right place at the right time, and who independently reported seeing the tiger.

You ran the numbers and landed at a new confidence of 8.7%.

That might still sound low. But don’t miss what just happened:

You didn’t go from “impossible” to “absolute certainty.”
You went from 1 in 10,000 to 1 in 11.5.

That’s a nearly 900-fold increase in confidence from just 3 text messages! You’re not just entertaining the idea anymore. You’re probably looking at the news, doing some google searches, texting other people, maybe even locking your door. That’s what rational belief revision looks like.

But What If More Evidence Came In?

Now imagine this:

You open your local news app—it confirms the tiger report.

There is video footage of the TIger.

Police issue a public safety alert.
Another group chat says animal control has been dispatched.

None of this is speculative. It’s concrete, independent, and mutually reinforcing. You update again.

This time, your confidence doesn’t just move to 15% or 30%.

It skyrockets to ~99%.

You’ve gone from “No way” to “I’m convinced.”

Why Spend So Much Time Explaining This?

You might be thinking: Okay, cool math. But why does this actually matter?

Because this isn’t just about tigers. Or resurrections.

It’s about how we reason—especially when the claim is surprising, uncomfortable, or unprecedented.

Bayes’ Theorem doesn’t force you to believe in miracles.
It doesn’t demand that you accept anything supernatural.
It simply formalizes one key question:

Which explanation makes the evidence more expected?

And that’s the crux.

Even if your prior confidence in a claim is extremely low, strong enough evidence can shift the scale—if that evidence is far more likely under one hypothesis than its competitors.

That’s exactly what happened in the tiger example.

You began with a skeptical prior. But when multiple pieces of independent evidence converged—school closures, news alerts, trusted witnesses, animal control—you didn’t say:

“Tigers don’t live in the suburbs, so this can’t be real.”

You let the evidence move you.

That’s the difference between reasoning and rationalizing.
One adjusts belief based on what is expected. The other blocks belief based on what is allowed.

Bayes doesn’t care how strange the claim is.
It only cares how well the claim explains what we actually see.

Let that sink in.

This Is Exactly What We’ll Do With the Resurrection

We’ll now walk through the resurrection case just like we did here—one piece of evidence at a time.

We’ll start with extreme skepticism.
We’ll examine each data point: group appearances, hostile conversions, the empty tomb, the transformation of a movement.
And we’ll let the evidence speak.

Not once will we assume the conclusion.
We’ll simply ask, at every step:

“Given this, what’s more expected—naturalism or resurrection?”

You’ll see how, even with the lowest possible prior, the explanatory power of the resurrection makes it very hard to ignore.

Let’s begin.

Applying This To The Resurrection

So far, we've seen how Bayesian reasoning helps us assess surprising claims—not by gut instinct or metaphysical veto, but by comparing how well different explanations account for the evidence.

Now, let’s plug the resurrection of Jesus into that framework. I want to be upfront about something though. In this section, I am granting a low prior probability for the resurrection for sake of simplicity. We will challenge that soon.

We start with our structure (odds form with labels for resurrection and alternative theories):

Where:

• E = the body of historical evidence

• HRes = the resurrection hypothesis

• HAlt = any competing hypothesis (e.g., hallucination theory, theft, legend)

• k = relevant background knowledge, including the HRM (i.e., that the Gospels are good-faith historical reportage)

Now the question becomes:

How much more expected is this body of evidence if Jesus really rose from the dead, versus if He didn’t?

This is our likelihood ratio—and it's the heart of the analysis.

Let’s consider just a few of the facts that I, and many other Max Data & HRM proponents argue for. As a reminder, the purpose here is not to give you the full fledged case here.

Jesus was crucified under Pontius Pilate.
His tomb was later found empty.
His followers experienced what they interpreted as post-mortem appearances of Jesus—individually, in groups, and in varying contexts.
These experiences transformed frightened followers into bold proclaimers of resurrection, even in the face of persecution and death.
These experiences were multi-sensory in nature.
This proclamation emerged in Jerusalem (the very place Jesus was buried), spread rapidly across the Roman world, and became the central claim of the earliest Christian confession.
This proclamation happened both in an anticipated hostile environment and continued to happen with voluntary suffering for no earthly reward.
and much more.

Now ask:

Would we expect all of that if Jesus stayed dead?

Would hallucinations alone explain group appearances, polymodal encounters, the empty tomb, and the explosive, resurrection-centered birth of the early church?

Would the hallucination theory predict this kind of geographical and chronological proximity between event and proclamation?

Would a conspiracy account explain the disciples’ willingness to suffer if they knew it was a lie?

The resurrection hypothesis isn’t just “one more possibility.”
It renders the totality of this evidence far more expected than any alternative.

And here’s the crucial point:

Even if you assign a skeptical prior to the resurrection, a high likelihood ratio still increases its posterior probability.

Just like the tiger in the neighborhood: your initial skepticism is real. But as the evidence rolls in—video, school closures, news alerts—you update.

Bayes doesn’t ask if you like the idea. It asks if the evidence fits.

And in the case of Jesus’ resurrection, the fit is striking.

That doesn’t mean the resurrection is proven with 100% certainty. Historical claims rarely are.
But it does mean this: If you're reasoning honestly, you can’t dismiss the resurrection by fiat.

You have to engage the data.

And if the resurrection hypothesis makes that data more probable than any alternative, it earns its place at the table—not as dogma, but as the best explanation available.

Running The Numbers

Just like our tiger example above, lets run through the topic of the resurrection with the formula. We’ve laid out the logic of Bayesian reasoning, shown how priors and likelihoods interact, and demonstrated how even unlikely hypotheses can become probable if the evidence is strong enough.

Now let’s apply that same structure to the resurrection of Jesus.

But as a reminder of two things:

First, we’re going to grant the skeptic a low prior up front. We’ll assume the lowest plausible prior imaginable—1 in a million. Why? Because if the resurrection can survive that, it can survive anything.

Second, the HRM is part of our background knowledge in this Bayesian framework—not the conclusion we’re trying to reach. It’s what allows us to assign likelihoods to what the documents say. We're asking: Given that the Gospels and Acts report claims sincerely made by early individuals, how likely is it that those individuals encountered a risen Jesus, versus being deceived, hallucinating, or lying?

Let’s walk through it, step by step—just like we did with the tiger

Define the Hypotheses

HRes = the resurrection hypothesis

HAlt = any competing hypothesis (e.g., hallucination theory, theft, legend)

Set the Initial Prior

We’ll grant:

P(HRes) = 0.000001

P(HAlt) = 0.999999

Now calculate the prior odds:

To clarify: We’re not starting with the assumption that the resurrection is likely. We’re assuming it’s extremely improbable and seeing if the evidence can still move the needle.

Sequential Evidential Updates

Now we’ll begin walking through the historical evidence—step by step—updating our probabilities as we go.

Each piece of data has already been defended in detail elsewhere (by myself and others), so we won’t re-argue every point here. Instead, the goal is to demonstrate how cumulative evidence functions within the Bayesian framework.

This is where the strength of the Historical Reportage Model—and the Maximal Data Approach—becomes clear.

Even starting from radical skepticism, the evidence doesn’t just nudge belief in the resurrection. It dramatically shifts the odds in its favor.

Lets begin!

Evidence 1: Jesus’ Crucifixion under Pontius Pilate

Virtually all scholars—across theological and skeptical lines—agree that Jesus was crucified under Pontius Pilate. This is highly expected under both P(HRes) and P(HAlt) so it does not shift our odds…but we’ll still plug it in for practice.

As a reminder, this is the odds form of Bayes.

Step 1: Assign Prior Odds

Then we calculate:

Step 2: Evaluate the Evidence and Compute the Likelihood Ratio

The evidence was equally as likely so…

Then we calculate:

Step 3: Multiply by the Prior Odds

Start with the full structure:

Now insert the numbers:

Step 4: Convert to Posterior Probability

Step 5: Interpret the Result

We started with radical skepticism: just a 1 in 1,000,000 chance that Jesus rose from the dead.

Then we evaluated the first data point — Jesus’ crucifixion under Pontius Pilate.

It didn’t move the probability. Why?

Because this event is highly expected on both hypotheses. The data is again equally probable on both the resurrection and any skeptical alternative hypothesis. I only inserted this so we can practice. The next piece of evidence is not neutral at all however.

Evidence 2: Multi-Modal Post-Mortem Appearances (to 21 Individuals)

This is where the Bayesian scales start to tip.

Following Jesus’ death, multiple groups and individuals — totaling at least 21 distinct reports found across the Gospels and Acts — are described as having seen, heard, and physically interacted with the risen Jesus. These are not vague references to dreams or isolated inner impressions. They involve multi-modal, multi-sensory encounters over a forty-day window, often in collective settings with multiple participants.

Now, crucially:
This is not an argument that “the Bible says it, therefore it happened.” That would be circular.

Rather, we are granting the Historical Reportage Model (HRM) — the view that the Gospel and Acts authors intended to report sincere, eyewitness-rooted historical testimony. We’re taking these appearance claims as what people actually said happened, and asking how expected those reports are under each hypothesis.

So: Given that these accounts most likely reflect sincere testimony from individuals who believed they encountered the risen Jesus — how likely is this body of testimony under Hₐₗₜ, compared to Hᵣₑₛ?

Let’s run the numbers and see how this evidence affects our confidence.