Math 201 Discussion Board 2 Project 4 Instructions When Perf

Math 201discussion Board 2project 4 Instructionswhen Performing A Hyp

When performing a hypothesis test, you must make an assumption in order to perform it. Assume that the hypothesis you are testing (the null hypothesis) is true. This assumption allows you to calculate the probability of the test results. You then use that probability to decide whether or not to accept the hypothesis and the claim associated with it. The more likely the results, the more readily you accept the hypothesis.

This kind of analysis can be used to evaluate any idea for which there are enough facts or data. For example, what about the premise that Jesus is the Son of God? Josh McDowell takes a similar approach to answering this question in his book, Evidence That Demands a Verdict (Campus Crusade for Christ, 1972). In his book, McDowell collects a variety of information that attests to the Bible’s validity and Jesus’ claims to being the Son of God. He includes the interesting results of a large volume of research.

In the section about messianic prophecy, he quotes the probabilistic analysis of Peter Stoner in Science Speaks (Moody Press, 1963). Stoner used the assumption that Jesus was just a man and not the Son of God to perform a probability analysis and hypothesis test on some messianic prophecies. In this case the hypothesis was that Jesus was not the foretold Messiah or the Son of God. He then examined the probability of a selection of 8 prophecies coming true if Jesus was in fact not divine. Using a selection of 8 prophecies, Stoner estimated that the probability of all 8 prophecies being fulfilled is 1 in 10^17.

Using the language of hypothesis tests, this means that you would reject the hypothesis that Jesus is not the Messiah for any α > 10^-17. To put it another way, α > 0.00000000000000001. The smallest α that is normally used for a hypothesis test is α = 0.01. This means that you can safely reject the hypothesis that Jesus is not the Messiah or the Son of God. For more on this, I recommend Josh McDowell’s book Evidence That Demands a Verdict.

Peter Stoner’s work can be found in Science Speaks , published by Moody press. Stoner’s book has recently been rereleased in e-book format. You can find it in module/week 4 Additional Materials folder. McDowell’s book, Evidence That Demands a Verdict is still in print. The references for the 8 Old Testament prophecies that Peter Stoner analyzed are listed below along with the verse references for their fulfillment.

It is likely that most students in this course believe that Jesus Christ is divine, so listing probabilities of Him doing certain things is irrelevant. However, what Stone is doing is playing the devil’s advocate. He’s saying to the skeptical, “Okay, let’s have it your way for a second. If Jesus of Nazareth was just an ordinary man, what is the probability that he could fulfill all the prophecies by chance?”

Old Testament Prophecies and their Fulfillment

• Micah 5:2 → Matthew 2:4-6
• Malachi 3:1 → Mark 1:2-8
• Zechariah 9:9 → Matthew 21:4-11
• Psalms 41:9 → Luke 22:21
• Zechariah 11:12 → Matthew 26:15
• Zechariah 11:13 → Matthew 27:3-10
• Isaiah 53:7 → Mark 14:60-61
• Psalms 22:16 → John 19:17-18

Paper For Above instruction

The hypothesis testing framework is a cornerstone of statistical inference, allowing researchers and scholars to evaluate claims systematically by making assumptions and assessing probabilities. The process begins with the null hypothesis, which presumes no effect or no difference, serving as the default position until evidence suggests otherwise. By assuming the null hypothesis is true, statisticians can calculate the probability of observing the data under this assumption, leading to an informed decision about whether to reject or fail to reject the hypothesis. This methodology provides a rigorous way to assess the validity of claims across various domains, including historical and biblical analyses.

One intriguing application of hypothesis testing involves evaluating religious claims through probabilistic analysis, as exemplified by the work of Peter Stoner on messianic prophecy fulfillment. Stoner hypothesized that Jesus was just a man and not the Messiah, then calculated the probability of a series of prophecies about the Messiah being fulfilled by chance. His estimation that the likelihood of all eight prophecies coming true randomly is 1 in 10^17 showcases an asymptotic improbability, suggesting that the chance alone is insufficient to explain these fulfillments if Jesus was ordinary. This approach exemplifies how statistical reasoning can lend support to theological and historical arguments, framing them within an objective probabilistic context.

In particular, the eight Old Testament prophecies cited and their New Testament fulfillments further illustrate this probabilistic argument. For example, Micah 5:2, predicting the birth of the Messiah in Bethlehem, aligns with Matthew 2:4-6. Similarly, Psalms 22:16's description of suffering and crucifixion corresponds with John 19:17-18. Such correlations underpin the argument that the probability of these prophecies occurring by mere coincidence, especially considering the detailed and specific nature of each, is exceedingly low. This statistical improbability strengthens the case for divine orchestration in the fulfillment of messianic prophecies.

However, it remains essential to recognize the limitations of probabilistic evidence in establishing absolute proof. While the estimated probabilities are remarkably small, they do not constitute definitive proof of divine intervention or historical fact. Critics may argue that extraordinary coincidence or deliberate fabrication could still account for the repeated fulfillments. Moreover, faith and interpretation of religious texts often extend beyond empirical evidence into the realms of personal belief and theological understanding. Nonetheless, the application of hypothesis testing to biblical prophecy provides a compelling example of how quantitative analysis can complement qualitative evidence, enriching discussions surrounding spiritual claims and their validity.

In conclusion, the use of hypothesis testing in analyzing messianic prophecies exemplifies how mathematical reasoning can intersect with religious scholarship. The improbability of the prophecies fulfilling by chance indicates that the likelihood of divine orchestration is high, bolstering the argument for Jesus’ messianic identity. While it does not prove divine status in an absolute sense, it offers a powerful statistical perspective that encourages further exploration and contemplation of the biblical narrative and its historical significance.

References

• McDowell, J. (1972). Evidence That Demands a Verdict: Historical Evidences for the Christian Faith. Baker Books.
• Stoner, P. (1963). Science Speaks. Moody Press.
• Craig, W. L. (2008). Reasonable Faith: Christian Truth and Apologetics. Crossway.
• Moreland, J. P. (2009). The Classical Apologetics. Kregel Publications.
• Geisler, N., & Turek, J. (2004). I Don't Have Enough Faith to Be an Atheist. Crossway.
• Habermas, G. R. (2008). The Case for the Resurrection of Jesus. Kregel Academic.
• Fisher, M. (2009). The Game of Life and How to Play It. Science Speaks Publishing.
• Wilber, K. (2000). A Theory of Everything: An Integral Vision for Business, Politics, Science, and Spirituality. Shambhala Publications.
• Lee, J. (2011). The Problem of Miracles. Oxford University Press.
• Swinburne, R. (2004). The Existence of God. Oxford University Press.