Last week I
published my review of Probability Zero: The Mathematical Impossibility Of
Evolution by Natural Selection by Vox Day. Since then he has already
published a sequel called the Frozen Gene. Vox Day’s books are currently
best sellers in the Science section of Amazon and deservedly so. The first one, and
what I have read of the second one, very powerfully demonstrate that the proposed
mechanisms of naturalistic evolution simply do not work. They do not have the
horsepower. None of the modified versions of the theory do either. The math is
simply not in their favour.
Vox Day has
done the math, checked it with advanced AI’s and leading physicists and other
scientists, and put the conclusive nails in the evolutionary theory coffin. His
books are causing a bit of storm right now, because they are challenging the
current scientific narrative about the scientific explanations of bio-diversity
in our world. But as Vox Day notes in his book, he is not the first person to observe
the mathematical impossibility of evolution. He has in fact independently come
across the mathematical challenges to evolution that other scientists and
mathematicians have discovered over the years. Many people who examine the
data, and know what they are looking at, find that evolutionary theory crumbles
to dust under closer examination.
A good
example of another mathematical case against evolution is this one here, from Replacing
Darwin, a 2017 book by Nathaniel T Jeanson. Replacing Darwin comes
at this from a very different perspective than Vox Days, but it is another
thread of evidence showing that the mathematics do not support the theory of
evolution. It is also a good bit of evidence to show that among the creationist
researchers fellowship there are genuine scientists doing the kind of research
that really helps put the naturalistic worldview to the test.
Answers In
Genesis and other
such creationist ministries are often overlooked by many Christians and seen as
anti-intellectual. But they have some seriously good scientists and researchers
in their ranks, and they are working hard and fighting an uphill battle. I
think in the long run many of their claims will be vindicated. In Probability
Zero Vox was clear that he was not seeking to make a classical creationist
argument. He is simply seeking to demonstrate that the naturalistic explanation
for biodiversity does not work. He also proposes an alternative, Intelligent
Genetic Manipulation.
Jeason,
however, is clear that he is coming from a creationist perspective. What he is
able to demonstrate with genetics and mathematics, is that the young earth
creationist perspective is not as unscientific as many people have said it is. In
fact, it makes proper predictions that can actually be tested and quantified.
I have here
an extended discussion from his book, which I read many years ago. I went back
over this section of the book a few days ago, to evaluate it in light of Day’s
observations and calculations. You will see his argument is different to Day’s
though it does overlap in some ways.
I’ll let
Jeason outline his argument for you, himself, here:
“Examination
of current evolutionary literature reveals that the assumption of constant
rates of change is largely followed. When discussing molecular clocks,
evolutionists typically measure the DNA difference between two species, assign
the time of origin from the evolutionary geologic timescale, and then calculate
a rate of mtDNA mutation from these parameters. Implicitly, this methodology
assumes constant rates of mtDNA mutation.
However,
very few evolutionary clock analyses invoke the measured rates of mtDNA change.
By analogy, the typical evolutionary molecular clock methods parallel the
following (theoretical) geologic practice: Let’s say a geologist wants to know
the rate of erosion in the Grand Canyon.
Rather
than measure it directly, the geologist first determines the ages of the layers
in the Grand Canyon. Then the geologist determines the depth of the Grand
Canyon. By dividing the depth by the ages, the geologist calculates how fast
(or slow) the Colorado River has been eroding the gorge. Obviously, this “rate”
is simply a prediction, not an actual measurement. In practice, geologists
determine the rate of erosion by directly measuring it in real time. This
measurement directly tests the prediction we just made.
Similarly,
the rates of mutation in typical evolutionary molecular clock discussions
represent a prediction, not an actual measurement. This prediction can be
tested with the human pedigree-derived rate that we just discussed.
Using
these experimentally derived rates, we can make predictions on the origin of
humans. For example, by taking the evolutionary time of origin for humans or
for other species from the fossil record and by multiplying the time by the
mutation rate, we can predict how many mtDNA differences should be present
today. For comparisons between individuals in the same species, this math and
methodology is the same as that which the evolutionists have been using for
years. In technical terms, the equation is a coalescence calculation.
When
we’re comparing mtDNA differences between two separate species, we multiply our
calculation by 2 — to account for the fact that mtDNA differences have been
accumulating independently in both species. In technical terms, this second
equation is a divergence calculation. With respect to humans, evolutionists
have proposed that chimpanzees are our closest living relatives. They have put
the time of divergence between the human and chimpanzee lineages around 4.5 to
17 million years ago. Using this timescale, along with the measured human mtDNA
mutation rate, we can predict how many mtDNA differences should exist between
humans and chimpanzees today.
Before
we can perform this calculation, the mutation rate that I reported earlier must
be converted to an absolute timescale. To convert units of mutations per
generation to units of mutations per year, we need to know the ages at which
humans and chimpanzees give birth.
In
technical terms, the length of time from conception to reproductive maturity is
referred to as the generation time. Specifically, since mtDNA is inherited
primarily — if not exclusively — through the maternal lineage, we need to know
the generation times for female humans and female chimpanzees. For chimpanzee
females, the average generation time is around 25 years. In humans, the
generation time varies. Some women give birth early in life; others, late in
life. Since we’re calculating mutations over many generations, the safest
approach is to predict mutations over a whole range of generation times — from
15 years to 50 years. In practical terms, this means that humans mutate one
mtDNA base pair every 76 to 419 years.
Using
this rate, we can predict how many mtDNA differences should exist between
humans and chimpanzees after 4.5 to 17 million years of mutation. Though the
chimpanzee mtDNA mutation rate has not yet been empirically measured, we will
assume that it is the same as the human mutation rate.* Since we’re comparing
the DNA of two species to one another, a divergence calculation is most
appropriate. At a mutation rate of one base pair per 76 to 419 years, a minimum
of 21,480 mtDNA differences (1 mutation per 419 years * 4.5 million years * 2 =
21,480) and a maximum of 447,368 mtDNA differences (1 mutation per 76 years *
17 million years * 2 = 447,368) would arise. Today, only 1,483 mtDNA
differences separate these two species. (See also Figure 7.3, which uses more
precise calculations, based on previously published work.) The evolutionary
timescale predicts mtDNA differences far in excess of what is observed.
These
results also raise an important question. In humans, the total length of mtDNA
sequence is less than 17,000 base pairs. How could over 447,000 mtDNA
differences arise between humans and chimpanzees?
In
practical terms, the 447,000 result is the number of predicted mutations. Since
the total mtDNA genome size is far less than 447,000 base pairs, each mtDNA
position would have been mutated multiple times over. In other words, the mtDNA
genome would have been mutationally saturated. Today, a comparison of human and
chimpanzee mtDNA reveals two genomes that are far from mutational saturation —
the 1,483 differences represent just 9% of the total human mtDNA genome length.
These
evolutionary predictions improve little if we narrow our focus to living and
extinct members of the genus Homo. For example, Neanderthals are classified
within the Homo genus, and a Neanderthal mtDNA sequence has been published.
Evolutionists put the split between the Neanderthal and modern human lineages
about 400,000 to 700,000 years ago. Treating them as members of the same
species, we can use a coalescence calculation to predict how many mtDNA
differences should exist today between Neanderthal sequences and sequences from
living humans. At a mutation rate of one base pair per 76 to 419 years, a
minimum of 955 mtDNA differences (1 mutation per 419 years * 400,000 years =
955) and a maximum of 9,211 mtDNA differences (1 mutation per 76 years *
700,000 = 9,211) would arise. Today, only 213 mtDNA differences separate
Neanderthals and modern humans. (See also Figure 7.4, which uses more precise
calculations based on previously published work.51) Again, the evolutionary
timescale predicts mtDNA differences far in excess of what is observed. The
discrepancy between predictions and reality is less than what we observed for
the human-chimpanzee calculations. But it still fails to capture actual
differences.
When
we focus just on differences among modern humans, the discrepancy becomes even
smaller — but still fails to result in a successful prediction. As mentioned
above, evolutionists put the origin of Homo sapiens in Africa about 200,000
years ago. Since we’re examining differences within a single species, a
coalescence calculation applies. At a mutation rate of one base pair per 76 to
419 years, a minimum of 477 mtDNA differences (1 mutation per 419 years *
200,000 years = 477) and a maximum of 2,632 mtDNA differences (1 mutation per
76 years * 200,000 = 2,632) would arise. Today, an average of 77 mtDNA
differences separate African mtDNA sequences from other mtDNA sequences. An
average of 39 mtDNA differences separate non-African sequences from other mtDNA
sequences52 (see also Figure 7.5). The evolutionary timescale still fails to
accurately predict reality.
If
these predictions are unable to account for mtDNA differences that we see
today, what model can accurately predict them? If we expand our analysis
further back into evolutionary time and include more primate species, then the
number of differences in the “Actual” column would increase. However, the
longer timescale would necessarily lead to a higher number of predicted
differences. Since the mutation predictions for the human-chimpanzee timescale
already exceed the mtDNA genome size, this lengthening of the timescale would
only make the predictions even more at odds with reality.
On
the other side of the timescale spectrum, we might be able to make accurate
predictions for a very narrow group of modern humans. Perhaps the recent origin
of one of the European ethnolinguistic groups will be explicable by the
mutation rates we’ve discussed. But if this is all that the evolutionary
timescale can explain, what do we do with the rest of the timescale for human
evolution?
Can
the timescale itself be changed? In theory, perhaps this is possible. However,
in practice, this would require significant reinterpretation of the
conventional evolutionary geologic model — an action which could produce
significant disarray in this discipline.
In
a similar vein, perhaps the assumption of constant rates of change could be
altered. However, as we observed above, evolutionists have insisted for years
that changing rates must not be invoked to explain the majority of phenomena
observed in geology and astronomy. Instead, they have claimed that present
rates are the key to the past, and that the world we see today has arisen
primarily by slow, constant rates over time. Invoking changing rates in
genetics would be logically inconsistent with the practice of evolutionary
geology and astronomy.
Perhaps
the explanation involves natural selection. At first pass, this might seem
plausible. After all, mtDNA encodes proteins with critical functions in the
cell. If you interrupt basic metabolism, cellular death is sure to result.
Surely most of the thousands of mtDNA mutations that have occurred over the
last several million years of evolutionary time were lethal to the possessors
of these mutations. Consequently, natural selection would surely have
eliminated these mutations (and individuals) from the mtDNA pool.
How
might we evaluate the natural selection hypothesis? The scientific community
has a long-established practice of dealing with scientific controversies. We’ve
already discussed in chapter 4 how to advance a scientific debate towards
resolution. The scientific method operates like a process of elimination. When
two hypotheses offer competing explanations for the same phenomenon, one must
be eliminated before scientific inferences can be made.
Naturally,
this logic assumes that two competing hypotheses actually make testable
predictions. We assumed as much in our discussion of the history of genetics
(chapter 2–3) and in our discussion of Darwin’s arguments from biogeography.
For example, Mendel was successful as a scientist because he inferred rules
that made testable, accurate predictions about the mathematical ratios of
traits among offspring in each pea plant generation. As another example, in our
discussion of whether DNA or proteins were the substance of heredity, we
observed that both of these hypotheses made testable predictions. If proteins
were the substance of heredity, their chemical elimination in the experiments
of Avery and colleagues should have eliminated the transforming ability of the
heat-killed smooth cells. The same prediction follows from the hypothesis that
DNA is substance of heredity. Conversely, if species were created in their
present locations, then you might expect the fauna on islands to possess more
terrestrial species. You wouldn’t expect the native fauna to be so skewed
towards aquatic and aerial species. In other words, the hypothesis of the
fixity of species’ geography makes testable predictions.
Hypotheses
that fail to make predictions do not qualify as science. As evolutionists
maintain to this day:
Science
is . . . a process of acquiring an understanding of natural
phenomena. This process consists largely of posing hypotheses and testing them
with observational or experimental evidence. . . . Scientific
research requires that we have some way of testing hypotheses based on
experimental observational data. The most important feature of scientific
hypotheses is that they are testable [emphasis his].
The
importance of this fact to the evolutionary community is manifest in the way in
which it has been applied to creationist ideas:
Science
differs in this way [see quote above] from creationism, which does not use
evidence to test its claims, does not allow evidence to shake its a priori
commitment to certain beliefs, and does not grow in its capacity to explain the
natural world. Unshakeable belief despite reason or evidence (i.e., faith) may
be considered a virtue in a religious framework, but is precisely antithetical
to the practice of science.
In
other words, since the most important feature of a scientific hypothesis is
that it is testable, the seeming un-testability of the existence of God, of the
supernatural creation of various creatures, and of a global flood a few
thousand years ago has typically removed creationist ideas from the realm of
science.
Some
evolutionists have even taken the criticism of the creation model one step
further. They have summed up creationist views in a short phrase: “God did it.”
Besides rejecting this phrase as unscientific, they have denounced it as
anti-scientific. For example, let’s say that you were testing a potential
anti-cancer drug in the lab. If you were laboring over a confounding
experimental result, “God did it” wouldn’t seem to reveal an answer. At least,
it wouldn’t lead to discoveries on how the natural world operated. Rather,
testable hypotheses would be the only scientific way forward toward a solution.
In
light of this historical practice, we can revisit the evolutionary explanation
of natural selection. The elimination of thousands of mtDNA mutations by
natural selection might seem plausible. But to be scientific, this explanation
would have to make testable predictions. For example, the mtDNA mutation rate
in the most divergent African people groups (San peoples, Biaka peoples, etc.)
has not yet been measured. Can the evolutionary explanation of natural
selection predict what this rate will be? In other words, before the rate is
actually measured, will evolutionists publish a guess as to what it will be? If
not, is the evolutionary explanation scientific?
* * * *
Curiously,
the human mtDNA data that we’ve just discussed fits a model that many have
previously discounted. In a previous section, I discussed the YEC geologists
and astronomers who hold to a 6,000-year timescale for the earth and universe.
Predicting mtDNA differences for Homo individuals over 6,000 years exactly
captures both the average mtDNA differences among non-Africans and among
Africans (Figure 7.6).
The
non-African differences were best predicted by a moderate generation time
(i.e., about 30 years), and the African differences by a fast generation time
(i.e., about 15 years) (Figure 7.6). Historical data offered an explanation as
to why. Since mtDNA is inherited primarily — if not exclusively — through the
maternal lineage, data on female generation times are the most relevant to our
analyses. United Nations marriage data from the 1970s revealed that women from
African nations married younger than women from non-African nations (Table
7.2). My mtDNA predictions suggest that this discrepancy was also true in the
centuries preceding the 1900s.
Alternatively,
these marriage data might simply be an artifact, and not a reflection of
historical practices among African people groups. Conversely, some African
lineages might mutate their mtDNA at a faster rate than non-African lineages.
Measurement of a form of genetic change (recombination — see chapter 9) in a
different DNA compartment (the nucleus — see chapter 8) suggests that Africans
have faster rates of genetic change than non-Africans.57 This might also be
true in the mtDNA compartment.
As
mentioned above, no direct measurement of the mtDNA mutation rate has been
performed in the most divergent African people groups. I expect that the rate
in these groups will be on the order of 1 mutation per 5 to 8 generations — or
faster. In fact, I wouldn’t be surprised if these divergent African lineages
mutate twice as fast as the non-African lineages — 1 mutation per 2.5 to 4
generations.
In
other words, the 6,000-year timescale makes testable predictions about the rate
of mtDNA mutation.[1]
Vox Day’s calculations
demonstrate that the theory of evolution by natural selection does not have enough
time or capability to be able to achieve what is necessary to explain biodiversity. The mechanisms
proposed simply do not have the required horsepower, and this is just a fact.
Jeanson’s calculations show that the evolutionary worldview predicts far more
changes in mtDNA than are actually observed. In other words, the naturalistic
time scales are wrong, and this is demonstrated by cross referencing changes in many different species.
Mathematics is squeezing the life out of evolution. It makes me wish that I had studied more mathematics in my earlier days. However, the equations being discussed here are relatively simple. Creationists have always argued that genetics was going to end evolutionary theory. Day has demonstrated that someone or something or somethings have manipulated human and other DNA on earth. As I said in my last review, evolution is dead, but its corpse will hang around for a while. But it is really starting to stink guys. Evolution was always pseudo-science. I recognized that when I started reading different evolutionists years ago and observed how much of their books were based on their imagination, rather than hard data. It was only a matter of time before enough data put an end to the credibility of evolution by natural selection.
[1] Jeanson
Ph.D., Nathaniel T. Replacing Darwin: The New Origin of Species (p. 293-307).
Master Books. Kindle Edition.
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