Results
Results appear here after submission. They are scenario estimates, not historical or theological proof.
| Metric | Value |
|---|---|
| Scenario | Study Scenario |
| Prophecies listed | 8 |
| Target fulfilled count | 8 |
| Dependence setting | 0.15 |
| Effective independent events | 3.902 |
| Independent joint probability | 3.5714 × 10^-21 |
| Independent odds | 1 in 2.8000 × 10^20 |
| Dependence-adjusted probability | 1.0611 × 10^-10 |
| Dependence-adjusted odds | 1 in 9.4237 × 10^9 |
| Range low | 3.4531 × 10^-11 |
| Range high | 2.5349 × 10^-10 |
| Probability of at least target count | 3.5714e-21 |
| Expected random fulfilled items | 0.043357 |
| Standard deviation | 0.207254 |
Calculator Inputs
Example Data Table
| Item | Example estimate | Accepted entry | Reason for caution |
|---|---|---|---|
| Birthplace claim | 1 in 2,800 | Born in Bethlehem | 1 in 2800 | Population assumptions vary. |
| Lineage claim | 1 in 1,000 | Davidic lineage | 1/1000 | Records and definitions matter. |
| Public entry claim | 1 in 100 | Public entry | 0.01 | Events may be related. |
| Betrayal price claim | 1 in 1,000 | Betrayal price | 0.1% | Interpretations differ. |
| Burial claim | 1 in 1,000 | Burial with rich | 1000 | A single number is uncertain. |
Formula Used
Independent model: P(all) = p1 × p2 × p3 × ... × pn
Dependence-adjusted count: n_eff = n / (1 + (n - 1) × ρ)
Adjusted model: log(P_adjusted) = log(P_independent) × (n_eff / n)
Expected fulfilled count: E = Σp_i
Variance: Var = Σp_i(1 - p_i)
At-least target: calculated with a Bernoulli dynamic program. It sums probabilities for exactly k through n fulfilled items.
How to Use This Calculator
- Enter one prophecy or event estimate on each line.
- Use a label, a vertical bar, and the probability value.
- Set dependence higher when items are not fully separate.
- Add uncertainty to test conservative and generous ranges.
- Choose an at-least target for partial fulfillment analysis.
- Press the calculate button and review the result above the form.
- Use CSV or PDF buttons to save your scenario.
Understanding the Question
This calculator is a statistical model. It does not prove faith. It estimates how rare a combined set of events may be when each event is assigned a probability. The result depends on the inputs, the assumptions, and the way dependence is handled. That makes clear data entry very important.
Why Assumptions Matter
Prophecy probability is often discussed with large odds. Yet every estimate starts with a judgment. One person may rate a prophecy as one chance in one hundred. Another may rate it as one chance in ten thousand. Historical context, wording, population size, and interpretation can all change the number. For that reason, the tool lets you change each probability.
Independent and Dependent Events
A simple model multiplies probabilities. This works when events are independent. In real study, some prophecies may be related. Birthplace, lineage, timing, public actions, and recorded events may not be fully separate. The dependence setting reduces the effective number of independent events. Higher dependence makes the combined result less extreme.
Using Ranges
Single-point odds can create false confidence. The uncertainty option builds a low and high range around each probability. This is helpful when a source gives only rough estimates. The range shows how much the final answer can move when inputs change. A wide range means the conclusion is sensitive.
Reading the Output
The joint probability shows the chance of all listed events happening under your model. The odds view converts the same value into “one in N” language. The expected hits show how many events would be expected on average from the listed probabilities. The at-least target helps study partial fulfillment scenarios.
Good Study Practice
Use this calculator as a transparent worksheet. Enter sources beside your notes. Try conservative, moderate, and generous scenarios. Compare the output instead of relying on one dramatic number. Keep theological claims separate from statistical modeling. A clear model supports better discussion, but it cannot replace historical, textual, or spiritual interpretation.
Limitations
No calculator can remove debate about definitions. A prophecy may have several readings. A probability may be educated, but still uncertain. Treat the final figure as a scenario result, not as absolute proof.
FAQs
Does this calculator prove that Christ fulfilled prophecy?
No. It models probability from user-entered assumptions. It can support structured study, but it cannot prove a theological claim by itself.
Why does dependence matter?
Some events may be connected. If they are connected, multiplying every probability as fully independent can make the final result too extreme.
What does one in N mean?
It means the modeled probability is expressed as odds. For example, one in one thousand equals a probability of 0.001.
Can I enter my own prophecy estimates?
Yes. Replace the example lines with your own labels and estimates. Use decimal, percent, fraction, or one-in notation.
What is the uncertainty setting?
It widens each probability up and down by a chosen percentage. This shows how sensitive the final result is to rough inputs.
Why are logarithms used?
Very small probabilities can underflow normal numbers. Logarithms keep calculations stable and make tiny combined probabilities easier to display.
What is the at-least target?
It estimates the chance that at least a chosen number of listed events are fulfilled under the independent Bernoulli model.
Should I use conservative or generous inputs?
Use both. Comparing conservative, moderate, and generous scenarios gives a clearer picture than relying on one dramatic estimate.