This article replies to Kevin NonTradiCath’s 23-minute video (https://www.youtube.com/watch?v=0DxSZKVDtPQ) on the Kolbe Center article “Genetic Entropy Recorded in the Bible?” The video was produced by a YouTuber whose handle is Kevin NonTradiCath (aka Kevin or Kevin O’Connor). He produces videos critical of the Catholic Faith, taking on a host of Catholic topics, such as Our Lady of Fatima, Our Lady of Lourdes, Eucharistic miracles, Latin Mass, etc. He says he has a chemical-engineering degree and he is easy to listen to. That is not a small thing on YouTube. It is also not the same as answering a paper the way working scientists usually do. The internet treats a microphone and a published article as if they weighed the same. They don’t. Some comments under his videos show people shaken in their faith. If that is you, this is for you. We cannot go after every bad take on the Internet. We can answer this one.
1. Scholarship
One of our chief criticisms is that Kevin dissects an entire document but fails to acknowledge or discuss the twenty references cited by the authors. Those citations are how a reader sees that the authors are not making graphs in a vacuum. You can criticize a paper without discussing every footnote. However, you should not peel the footnotes off and then treat the leftover pages as the whole file. No matter the discipline, serious writers or scholars always welcome honest criticism. If you have time, the cleaner path is to ask the authors the questions you plan to discuss. Nobody owes anyone a phone call. It would still have been better.
2. Statistics
As law is to society, so statistics is to the sciences: both organize messy domains, and both are used to confer legitimacy on decisions and claims. That is why serious scientific (empirical) work usually treats statistical design and inference as part of the science, not window dressing applied at the end—or worse—as an afterthought. Misused statistics can make a description, model, or theory look firmer than it is—and sometimes those errors can be costly. Here the words of Mark Twain come to mind. “There are three kinds of lies: lies, damned lies, and statistics.”
Scientists use statistics for the following purposes: (1) to describe and (2) to predict. And the Kolbe article does some of both. It plots the post-Flood ages and reads the shape as a picture of genetic entropy. A chart does not create the pattern. It makes the pattern easier to see. If I listed average body weights of 18- to 21-year-olds by decade since 1900, a bar chart would help you understand it better. Leaving the chart out would not change the numbers or the conclusion. That is all a descriptive figure is doing until someone asks it to forecast a different series. Statistics then help grasp concepts.
3. Concepts
At 8:05 in the video, Kevin says he wants to “poke holes” in the article. Of course, he is free to do so, and that phrase tells you the posture. A better start might have been: here is the claim; here is where I think it fails.
At 4:11 he discusses the plot of declining lifespans against generation number—the same kind of figure JC Sanford uses as a visual parallel to a decay curve. The fitted equation is a power law y = 1064.7x−0.766, not a quadratic and not a true exponential. Kevin calls it both. The graph is meant to make the pattern of longevity against generation visible. It is not a map of every later population.
Around 15:00 he brings in life-expectancy figures from 1770–2020. Those are not the numbers the authors fitted. Their equation was built from reported ages of the post-Noah patriarchs. A power curve describes the points you give it. It is strongest in the middle of that data, not far beyond it. Modern life expectancy at birth is a different series. The authors did not leave it out by mistake. They were not modeling it.
At about 16:14 Kevin turns to trendlines and the pre-Flood patriarchs. He is no longer only restating their fit. He adds the Adam-to-Noah ages the published curve set aside, gives the line new constants, and treats the worse result as proof that the authors ignored a problem on purpose. That charge is too strong and misleading. The authors’ published curve describes the post-Noah list. It was not offered as one law from Adam to the present. A flat stretch before the Flood and a drop after it is a modeling choice the authors mention, not a hidden contradiction in the same table.
Genetic entropy, as described by Kolbe/Sanford, is a proposed reading of why those biblical ages fall after the Flood. Real lives also face war, famine, disease, and climate. A simple decay curve will not capture all of that. That is a limit of the model. It does not give Kevin a free hand to fold in a different kind of series and then call the authors dishonest because his new line looks worse. The fair question is whether the Kolbe/Sanford equation still does the job they claimed when you keep their kind of data and say where the fit stops.
His third objection begins: life expectancy is more complicated than “genetic purity,” “which sounds a little fascist, don’t you think?” That phrase is not in the Kolbe article. It is a jab. It does not test the graph. Near the end he says the authors “don’t really address any of what I said.” The article was written years before the video. Of course it does not answer him. A back-and-forth would have been more useful than a closing that sounds like score-settling.
At 21:10 he calls the piece theology dressed as science: poor science, theology in front. That is a verdict, not an argument. Saying it firmly does not make the equation a quadratic, and it does not turn 1770–2020 life expectancy into patriarchal ages.
4. Summary
The video gets three things wrong, and those three are sufficient.
a. It misnames the fit. The published curve is a power law, not a quadratic and not a true exponential.
b. It treats modern life expectancy at birth as if it were the next patriarchal ages. Those are different series. A curve describes the points used to fit it. It is not automatically a forecast of another dataset.
c. It treats a worse line after new points and new constants as proof that the authors hid a problem. Their published fit was to the post-Noah list. Adding Adam–Noah or 1770–2020 is a different test. You may run that test. You may not call the original paper dishonest for not having run yours.
None of that requires guessing at anyone’s temper. Read the Kolbe article for the actual claim: the listed ages after Noah fall in a regular way, and the authors read that as genetic entropy. Feel free to accept it, reject it, or narrow it. Do not let a wrong curve name, a swapped dataset, or a motive charge decide the question for you.
