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One of the most common types of anti-evolution argument is the probability-based argument, and its greatest strength is the fact that the average person does not know the concept of probability very well, if at all. Creationists have many creative ways of using public ignorance of probability to their advantage, but the most common trick is to attack abiogenesis: the process by which it is believed that the first self-replicating molecule was produced in the primeval seas, billions of years ago.

Creationist arguments tend to rely on a common misconception:
to use a poker analogy, they assume that if you come up with a
very good hand, then you must be cheating. "But isn't
that true" you might ask? If someone comes up with a very
good hand in poker, doesn't that mean he cheated? Not
necessarily. It's *possible*, but you would need better
evidence than that.

In order to understand the flaws in the creationist probability
argument, one must first understand how you compute probabilities.
This should not be above the level of the average person because
they teach the basic concepts of probability in high-school algebra
class (or at least they did back when I went to school; I don't
know if the weakening of educational standards has rotted away this
part of the curriculum yet). The basic concept is disarmingly
simple: if the process is random, then you simply *count the
total number of possibilities and then divide it by the number of
outcomes you're looking for*. The *execution*, however,
can be quite complex and involves many potential pitfalls.

In the following pages, we will examine the concepts of probability with several everyday examples, before looking at an example of a typical creationist probability-based argument where we can put these concepts to use.

When it comes to audience familiarity, the best examples of
random probability can be found in the world of gambling.
Therefore, two excellent examples are the lottery, and the game of
5-card poker. **Warning**: ahoy, there be mathematics here! If
you *hated* math in school, you might have trouble getting
through this page. If you think you won't be able to handle it,
feel free to run like a coward to Page
3, but be aware that by skipping this page, you are proving
that *you don't have what it takes to discuss probability at
even the most basic level*. Keep that in mind if you're a
creationist and you intend to write me an E-mail telling me how
wrong I am.

Lottery games are random (by law), so they are obviously a good
example of random probability. Let's take an example lottery
where you must pick 6 unique numbers in any order from 1 to 49.
Remember that the first step is to *count the number of possible
combinations*: there are 49 choices for the first number. You
can't pick the same number twice, so there are only 48 choices
for the second number, 47 choices for the third number, and so on.
Therefore, the total number of possible combinations is
**49*48*47*46*45*44** (or 49!/43! on your calculator), which
equals approximately **10.07 billion**. However, you can pick
the 6 numbers in any order, and there are **720** possible ways
to arrange 6 numbers (6*5*4*3*2*1, or 6! on your calculator; now
you know what that *x!* button is for). Therefore, the overall
probability is 10.07 billion divided by 720 orders, or 13.98
million (probability math short-hand for this whole process is
"49 choose 6", or 49C6). Therefore, the odds of any given
set of numbers coming up in this type of lottery are roughly **1
in 14 million**. And if you play *two* sets of numbers in
the same lottery draw, then your odds of winning are roughly 2 in
14 million, or 1 in 7 million.

**Note**: an important recurring formula in probability is
"x choose y", and it looks like this:

*x choose y = x!/(x-y)!/y!*

This formula, also expressed as xCy (eg- 49C6) will be used
henceforth in lieu of bulky expressions such as
(49*48*47*46*45)/(6*4*3*2*1). The exclamation mark stands for
"factorial". **Fun Fact**: if you enter "49
choose 6" in Google Search, it will automatically compute the
result for you. Try it!

It must be noted that your odds of winning the lottery do
*not* go up if you have been playing every week for the last
10 years. Did you notice that in the above calculation, no mention
whatsoever was made of previous plays? That is because previous
plays do not factor into the probability equation at all. If your
intuition tells you that your 10 years of prior play should count
for something, remember that if one is to learn about scientific
and mathematical concepts, one must first learn to *listen to the
equations, not your intuition*. Your intuition is the sum total
of your life experience *up to now*, and it is a very
unreliable guide when learning about new and unfamiliar things.

So now you know how to calculate the odds of drawing a
particular number in a lottery. Here's an exercise: if you
understood the preceding, then calculate the odds of any given set
of numbers coming up in a lottery where you pick 4 numbers from 1
to 39. But in this lottery, the order of the numbers *does*
matter, and you *can* pick the same number more than once.
Click here for the answer.

In the game of poker, you have 52 cards: 4 suits (clubs, spades,
hearts, and diamonds), with 13 cards (ranked from ace to king,
although ace can be either low or high) in each suit (no jokers or
other wildcards for now). Remember that the first step is to
calculate the number of possible combinations. You can't draw
the same card twice, and the order doesn't matter, so if you
draw a 5-card hand from a deck of 52 cards, then the math is
similar to the first lottery example: "52 choose 5" =
52C5 = **2598960**.

In order to determine the probability of drawing any
*particular* hand out of those 2598960 possibilities, you must
determine how many different examples of that hand exist. For
example, a royal flush is the five highest cards in any given suit,
from ten to ace, like this example:

Click on the cards to see all **4** royal flushes: one for
each suit. Since there are 4 royal flushes, the odds of a royal
flush are 4 in 2598960, or **1 in 649740**. Another way of
calculating the odds is 20/52*51C4.

For a trickier example, the odds of getting a triple (three of the same number) can be computed by determining how many triples exist in the deck. A triple, also known as "three of a kind", is three cards of the same rank, like this:

Click on any card to see all the combinations available. There
are **13** possible ranks of triple, from ace to king, and for
each rank, there are **4** ways to get a triple out of the four
available suits. Therefore, there are 13*4=**52** possible
triples. But we still have to pick the last two cards, don't
we? Remember that there are 52 cards, we've already used up 3,
and the 4th card of the same rank is off-limits because we
don't want four of a kind, so so there are 48 cards left to
choose from for our 1st unknown. For our 2nd unknown, we don't
want a card of the same rank as the 1st unknown because that would
be a full house (a triple and a double), so that means there are
only 44 cards left to choose from. Therefore, there are 48*44 ways
to pick our two unknowns which can be arranged in two orders (1-2
and 2-1), so the total number of combinations is 48*44/2, or
**1056**. Therefore, there are 52*1056=**54912** different
possible hands containing triples, so your odds of a triple are
54912 out of 2598960, or approximately **1 in 47**.

So now you have an idea of how to calculate poker odds. It's trickier than lottery odds, because when you work with poker odds, the total number of possibilities is just the beginning. Here's an exercise: if you understood the preceding, calculate the odds of drawing a full house. That's a triple and a double, and if you paid attention when we solved the triple, the answer should be easy. Click here for the answer.

If you're feeling confident, you can try two more exercises: first, determine the odds of a straight flush. That's five cards in sequential order, all from the same suit. The lowest card can be an ace (aces can be low or high) but it cannot be a 10, because that would make it a royal flush. Click here for the answer. For a more difficult challenge, try to determine the odds of getting a straight flush if you add two jokers to the deck, and make them both wildcards (a wildcard can be used to fill in for any other card, thus increasing the likelihood of getting a rare hand). Click here for the answer.

Hopefully, we learned how simple probability looks at first, but we also learned how tricky it can get. Think of how easily one could go astray when trying to calculate the odds of a triple. If we forgot that the last two cards could be in any order, we would have forgotten to divide by 2!, and our odds would be off by 100%.

So far, we've only looked at single events: a single lottery
draw or a single hand of poker. What about a *series* of
events? A common creationist tactic is to take a long list of
events, assume that they had to occur simultaneously, and then
declare that the odds against such an occurrence are astronomically
high. This is a quote from a typical creationist argument:

"Proteins are functional because the amino acids are arranged in a specific sequence, not just a random arrangement of left-handed amino acids. The formation of functional proteins at random could be likened to a monkey trying to type a page of Shakespeare using the 26 letters of the alphabet. Anyone knows that the monkey is not capable of accomplishing the task set before him."

That's a pretty impressive argument, right? It's been estimated that it would take longer than the age of the entire universe for a monkey to actually type a page of Shakespeare. But this argument contains a hidden assumption: that the entire page has to be typed at once, or that the entire amino acid sequence has to be assembled at once. What if you can build it piece by piece? What if our hypothetical monkey is allowed to keep typing letters until they assemble a word in the sequence, and then goes on to the next word? Does that change the odds?

As it turns out, it changes the odds tremendously. In order to illustrate this, let's go back to the world of gambling and look at a familiar object: dice. Now I know what you're thinking: you're thinking "This guy has an obsession with gambling. Maybe he should seek help." But leaving my personality flaws aside, let's consider the problem of rolling dice. Specifically, rolling 10 sixes. If you took ten dice in your hand and rolled them, how likely are you to get ten sixes? That's a pretty simple calculation: there are 6^10 possible combinations, so your odds are 1 in 60466176. Of course, you can always try it yourself. The following JavaScript widget will roll dice for you until you get ten sixes:

Did you try it? How long did you wait? At 2 rolls per second and more than sixty million combinations (since the widget keeps the dice in order), you might get lucky in roughly ... one year. Assuming you don't want to wait that long, hit the "Stop" button and try this new widget, which does the same thing but does it one die at a time instead of trying to get them all at once:

That didn't take too long at all, did it? So what was the
difference? The difference is that when you have a series of
events, there is really no need to assume they all happen
simultaneously. You can take them *one at a time*, adding on
sequentially. And when you do so, the odds collapse to relatively
tiny numbers because you no longer multiply them together: instead,
you add them one at a time, as they happen. So your odds of getting
the first six are 1 in 6: something that should take only a few
seconds. Then your odds of getting the next six are also 1 in 6,
which should also take only a few seconds. On average, you're
probably looking at around 30 seconds to get all ten sixes, as
compared to a year the other way.

When you consider the huge difference between treating sequential events as a series as opposed to a single simultaneous event, you begin to realize just how easily creationists can exaggerate the unlikelihood of series events in evolution, even if we assume that these events are completely random.

Now consider the famous "monkeys typing Shakespeare" argument. If we modified our little die-rolling widget to roll for letters and select good letters, what effect do you think that would have on the time required to reach the goal? Perhaps our monkey should start practising his typing.

- The forum user "IPeregrine", for content suggestions.
- Rob Dalton and forum users "Darth Holbytlan" and "Starglider", for debugging assistance.

In the previous two pages, we've looked at poker, the lottery, and rolls of the dice: all games of chance. But what if we're looking at a non-random game? It may be possible to derive probabilities, but you would need to know the nature of the non-random mechanism.

For example, let's look at a baseball player trying to hit a ball. This is a decidedly non-random situation; if the ball is moving through the middle of the stroke zone, the player reacts to it well, and swings his bat at the ideal time and in the ideal place, the probability of hitting the ball is 100%. Or, if the ballplayer completely misreads the pitch and swings at the wrong time in the wrong place, the probability of hitting the ball is 0%. This is why you cannot generate a reliable prediction for any given ballplayer in any given at-bat; in order to know the probability of him hitting the ball, you need to know factors which cannot be evaluated until after the fact.

"Ah, but we have batting averages" one might answer.
That is true; baseball fans have long compiled lists of batting
averages which indicate a player's past performance. Baseball
statisticians even break them down into specific situations
(batting against left-handed pitchers, batting against right-handed
pitchers, batting against this particular pitcher, etc). But you
still cannot generate a meaningful statistical prediction for any
given at-bat, because there are too many variables you cannot
evaluate. At *best*, you can look at past performance and
assume that future performance will match, even though you
*know* that many variables will change from year to year, game
to game, or even at-bat to at-bat.

Could it be possible to get highly reliable predictions from
baseball statistics? One would have to say yes ... if the game were
played by robots. The field of *statistical* probability is
not necessarily unreliable or unscientific, but it requires a high
degree of *experimental control* which is just not possible
with human beings playing baseball. If you could produce baseball
players who were far more consistent in their behaviour (like
robots), a more reliable set of statistical probabilities could
emerge. However, that's not much of a solution for baseball,
unless we switch to robot players. Barry Bonds is a good start in
that direction, but the total elimination of the human factor is
still many years away.

This is all rather disappointing compared to our nice clean
mathematical analyses of poker games and lottery tickets, but
unfortunately, that is the nature of reality. The mechanism of
poker games and lottery tickets is incredibly simple: random
selection from a precisely defined set. However, the mechanisms of
*real* events tend to be considerably more complex. If you
could nail down all of the variables and ensure that they remain
fixed (impossible in the case of baseball games, but possible to
within a high degree of accuracy for scientific experiments), you
could use past performance to predict future performance, but the
clean mathematical technique is extremely difficult to apply, if
not impossible.

So is it *ever* possible to evaluate a non-random
probability? Yes, but you need to know a fair bit about the
mechanism. When the meteorologists say there is a 70% chance of
precipitation, they are saying this based on highly detailed air
movements and temperatures, in conjunction with a large body of
research into weather patterns and a superb understanding of the
mechanism of precipitation. But there are other situations where
you can generate reliable probability estimates for non-random (or
more accurately, partially random) situations.

**Note**: at this point, those of you who are starting to
feel tired of the mathematics should probably skip ahead to the
next page, because we're about to do
some more work with poker and dice, and those of you who easily
suffer "math fatigue" will probably start tuning out, if
I didn't lose you already back on Page
2.

**Poker ... again.**

Still here? OK, let's manufacture an example of a non-random
probability which *can* be evaluated: suppose you are playing
a modified game of poker where you are required to discard any card
which is the same rank as a card you are already holding (for
example, if you draw a two of spades and a two of clubs, you have
to discard the two of clubs and pick up another card). Given this
rule, the probability of drawing a double, triple, four of a kind,
two pair, or a full house would suddenly drop to precisely zero.
But the odds of drawing other hands such as straight flushes would
go *up*, due to a reduced number of alternatives.

For example, let's take the royal flush: there are still
just 4 possible royal flushes, but the total number of combinations
has changed because so many hands have been outlawed. You
*could* calculate the number of doubles, triples, quadruples,
two-pairs, and full houses and then subtract them all from 2598960,
but that would be a lot of work. Luckily, there's a much easier
way to calculate the odds of a royal flush with our modified rules.
You start with the ten card, of which there are 4 in the deck out
of 52 cards, or **1 in 13**. To get the matching jack, you have
to pick **1 of 48** remaining cards (remember that 1 card has
already been drawn and the 3 other ten cards are off-limits, so
you've got 52-1-3 cards left). In similar fashion, you have to
pick **1 of 44** cards to get the matching queen, **1 of 40**
cards for the king, and **1 of 36** cards for the ace. And
finally, you have to divide by 5!=**120** orders. Therefore, the
odds are 13*48*44*40*36/120, or **1 in 329472**.

**Loaded dice.**

Another type of non-random probability is the weighted
probability. This is still "random", but *unevenly*
so. The classic example of weighted probability is drawn from
gambling (yes, gambling again), and it's known by the popular
name "loaded dice". Suppose you had dice which had a
small metal weight inside, making them three times more likely to
land a six than any other number. You could still calculate odds,
but if you assumed that the dice were normal, your predictions
would not match the results.

The simplest way to account for weighted probabilities is to "double-count". For example, if sixes are 3 times more likely than any other number, you simply assume there are 3 sixes in the dice. So instead of each number having a 1 in 6 probability of coming up, numbers one through five would have a 1 in 8 chance of coming up, and the number six would have a 3 in 8 chance of coming up.

Hopefully, we learned that it's very difficult to generate
non-random probability estimates unless you know precisely how the
non-random mechanism works. Let's say that in our poker
example, we knew that there were some special rules for the draw
*but we didn't know what those rules were*. It would
become *impossible* to determine the odds of drawing any
particular hand. Worse yet, suppose we didn't even know how
many cards were in the deck. Probability estimating is an extremely
complicated business, where the smallest unjustified assumption can
produce numbers which are *completely* wrong and where missing
information can make any kind of estimate impossible.

OK, let's review. We have learned three things here:

- It is fairly easy to calculate probabilities for a very simple and completely random mechanism, like playing the lottery. However, as we saw in our poker examples, it can get much trickier when you introduce more complexity.
- If you require a series of unlikely
events, there is an
*enormous*difference in probability if you treat them as a single simultaneous event instead of treating them as separate events, as demonstrated with the example of rolling dice. - As we saw in our modified poker
example, the instant you introduce as much as a
*single*draw rule of any kind, the draw mechanism becomes non-random. Once the mechanism becomes non-random, any probability calculation based on pure randomness will become useless.

Keep all of those facts in mind when examining any creationist
probability argument, because they almost always ignore *all*
of them. In fact, now that you are armed with a basic comprehension
of the difficulties inherent in probability calculation, you should
be able to see through the following common creationist tricks:

Creationist probability calculations are usually characterized
by their *extreme* simplicity, as if there's really
nothing more to probability than counting the numbers of entities
involved in a process and turning them into an exponential figure,
with no regard whatsoever for whether the process is characterized
by many valid picks or only one. For example, in poker, the odds of
drawing a royal flush are very low, but the odds of drawing a
double are very high, because there are so many doubles. When
creationists speak of the odds of evolution or abiogenesis, how do
they know they are looking for a royal flush or a double? How do
they know how many valid outcomes exist? Quite tellingly, they
never even *mention* this question, because it's so much
easier to assume that there is only one.

They also ignore the question of whether events in a series
should be treated as separate events or a single combined event.
And yet, as we saw in our "rolling dice" example, this is
an *enormously* important factor. If 500 binary events are
required for something, a typical creationist would
*literally* assume that you can compute the odds of this event
by simply punching "2^500" into a calculator, with no
regard whatsoever for the underlying mechanisms or the manner in
which these events should be combined.

We have already seen that there is a huge difference between
random probabilities and non-random probabilities. Unfortunately,
*most creationists falsely assume that all natural processes are
random*, which is why they usually describe evolution as
"random chance". And yet, we saw with the case of
modified poker that the addition of as little as *one* rule
can completely falsify random probability calculations! If this is
the case, then how much effect do all the myriad rules of organic
chemistry have? How can anyone seriously call organic chemical
reactions "random chance" when they have so many rules
that most people struggle to grasp the few rules they describe in
high-school chemistry?

The assumption of randomness is so deeply buried into creationist thought that almost no creationists even state it as an assumption; they simply incorporate it silently into all of their arguments and hope that you won't even think to ask the question.

Once we realize that we are dealing with non-random processes,
we run into a serious problem: as we saw in our modified poker
example, it would be *impossible* to calculate the odds of
drawing a royal flush if you don't account for the modified
draw rule, and it is impossible to account for the modified draw
rule if you don't know what it is. In a situation like this,
you cannot compute probabilities no matter how much of a skilled
mathematician you are.

Now that you (hopefully) understand how difficult it is to
generate probability estimates even for a situation where you know
most of the variables and mechanisms, how can you generate such
precise estimates for events where you *don't* know so
many of the variables? And why does the math behind these
creationist probability estimates look so nice and clean and
simple?

Are your suspicions raised yet? They *should* be.
You've seen how complicated a probability analysis can be for
something as simple as a game of poker. How can creationists
possibly derive such mathematically simple probability estimates
for something as remarkable as the origins and development of
biological life? Sure, you could say that their stratospherically
high probability estimates are perfectly reasonable for something
you find so alien, but that's not much of an answer, is it?
Probability estimates are most accurate when you know the situation
very well, not when you don't know it at all. The sheer
simplicity of their work is proof of its inaccuracy: the idea that
complex catalyzed organic chemical reactions could be modeled in
such a manner that they are far simpler than a mere poker game is
the height of absurdity, yet it is widely accepted practice in the
creationist world.

Now that we've seen how creationists typically operate, it's time to examine the phenomenon "in the wild", so to speak. A good example of a creationist probability argument can be found here . Here is an excerpt:

"In experiments attempting to synthesize amino acids, the products have been a mixture of right-handed and left-handed amino acids. (Amino acids, as well as other organic compounds, can exist in two forms which have the same chemical composition but are three-dimensional mirror images of each other; thus termed right and left-handed amino acids).

One would think that the formation of amino acids into protein would randomly use both left and right-handed amino acids and result in approximately 50 percent use of each. However, every protein in a living cell is composed entirely of left-handed amino acids, even though the right-handed isomer can react in the same way. Thus, if both right and left-handed amino acids are synthesized in this primitive organic soup, we are faced with the question of how life has used only the left-handed amino acids for proteins.

We can represent this dilemma by picturing a huge container filled with millions of white (left-handed amino acids) and black (right-handed amino acids) jelly beans. What would be the probability of a blind-folded person randomly picking out 410 white jelly beans (representing the average sized protein) and no black jelly beans? The odds that the first 410 jelly beans would be all one color are one in 2

^{410}."

Can you see how the creationist employs exactly the tricks we
could have predicted? The formation of amino acids in living
organisms is *obviously* non-random; why else would we
consistently produce only left-handed ones in our bodies? Almost
immediately, we see that he has confused a clearly non-random
process for a purely random one: a trick he attempts to gloss over
by noting that a totally *different* method for producing
amino acids is more random (this is known as the
"red-herring" fallacy).

"Ah", a creationist might retort, "but what about
the very *first* amino acids? According to evolutionist
beliefs, they weren't produced by living organisms!". That
would be correct, but of course, it hides yet another baseless
assumption on his part: that the very first amino acids were all
left-handed. How could he possibly know that? He admits himself
that left-handed and right-handed amino acids are functionally
identical, so there's no reason they had to be. His only reason
for this assumption is the fact that amino acids *formed in
living organisms today* are all left-handed, and as already
noted, that is obviously not a random process.

Here's another excerpt:

"Proteins are functional because the amino acids are arranged in a specific sequence, not just a random arrangement of left-handed amino acids. The formation of functional proteins at random could be likened to a monkey trying to type a page of Shakespeare using the 26 letters of the alphabet. Anyone knows that the monkey is not capable of accomplishing the task set before him.

What is the probability of synthesizing a protein with a specific sequence? Let us simplify the situation first. For example, if there are 17 students in a class, how many possible ways exist for them to order themselves in a line? It would take the students a long time to physically try all the possibilities since there are over 355 trillion different ways. If the number of students were increased to 20, equal to the number of amino acids that exist, the number of possible ways would be over 10

^{18}different ways, the number of seconds in 4.5 billion years!Remember: this is a simple example of a specific arrangement of 20 amino acids. The probability is even greater when we consider that there are 20 possibilities for each spot. Also, in a specific protein of 100 amino acids, or in the formation of a hemoglobin molecule which has 574 amino acids arranged in a specific sequence, the probability becomes astronomical!

If only one amino acid is changed in the sixth position, the disease sickle cell anemia results. The RNA within the tobacco mosaic virus contains about 6,000 nucleotides. The probability that this molecule resulted by the random chance arrangement of the four nucleotides is 1 out of 4

^{6000}or 2.3x10^{3216}!"

As one can see from this passage, creationists are almost entirely too predictable. This is really nothing more than the exact same deception he employed in the previous excerpt: assuming that organic chemistry has no rules and is completely random, even though we know that this is false. If you are observant, you may also notice that he assumes without explanation that the small number of amino acids we use are the only kind of amino acids that could possibly work, hence assuming that we're looking for the equivalent of a royal flush in poker. But how does he know the other combinations wouldn't work?

They wouldn't work in *our* environment because an
organism needs to be compatible with the other organisms around it,
but how does he know that in an alien environment, an entirely
different set of compounds could not have arisen? In other words,
how does he know we're looking for a royal flush instead of a
triple, which is much more common? The short answer is that he
doesn't; like the rest of his argument, it's nothing more
than an assumption on his part, used in order to produce an
inflated probability figure by heavily oversimplifying a complex
situation.

Here is another excerpt:

"Life is not contained within a single protein, however. Several proteins are required for even the basic functions of the simplest living organism. Even the most simple known cell, such as the mycoplasma, may have 750 proteins. The list of proteins essential for survival may be narrowed down to 238 proteins. The probability of forming these 239 proteins from left-handed amino acids has been calculated to be 1 in 10

^{29,345}.

It is perhaps telling that he does not divulge the method by
which he computed this figure, because it is almost comically
absurd. He starts with the false 2^{410} figure from
earlier, and he multiplies it by itself, 238 times. In short, not
only does he falsely assume that amino acid formation in living
organisms is totally random, but he assumes that every protein must
have been created simultaneously, like rolling 238 dice all at once
instead of rolling them one at a time. There comes a point where
one must seriously question whether a creationist argument is the
result of incompetence or deliberate deception, and this argument
is fast approaching that point.

Are you starting to see how creationists abuse the concept of
probability? Notice how they set you up by treating modern life and
ancient life as if they are interchangeable (making assumptions
about the most primitive early life forms based on modern life
forms, even though the simplest bacteria is *still* the result
of billions of years of evolution), and then they generate
preposterously large numbers by deceptively combining many events
into one and falsely assuming that organic chemistry has no rules
and is therefore totally random. Also notice how incredibly simple
their calculations always are; they make our poker analyses seem
like highly advanced mathematics by comparison. So why don't
you try examining the following excerpt for yourself, to see if you
can spot the tricks and oversimplifications?

Many times we hear evolutionists using the term "primitive cell," although we have no example of such. One of the simplest living systems, the tiny bacterial cell, is exceedingly complex. Dr. Michael Denton describes the bacterial cell, which weighs less than 10

^{-12}grams, as: "... in effect a veritable micro-miniaturized factory containing thousands of exquisitely designed pieces of intricate molecular machinery, made up of one thousand million atoms, far more complicated than any machine built by man and absolutely without parallel in the non-living world."Our human body has over 200,000 types of proteins in its cells, and the odds of just one of those proteins evolving by chance is vast. Sir Fred Hoyle, still an evolutionist, likens this to a blindfolded subject trying to solve the Rubik's cube. The blindfolded man has no way of knowing whether he is getting closer to the solution or actually farther away. According to Hoyle, if the blindfolded subject were to make one random move every second, it would take him on the average three hundred times the supposed age of the earth, 1.35 trillion years, to solve the cube.

Out of the 200,000 proteins in our body, roughly 2,000 provided the very essential function of cellular metabolism, similar to that in a bacterial cell. The odds of those essential enzymes arriving by chance is extremely large, almost improbable. As stated by Drs. Hoyle and Wickramasinghe, "the trouble is that there are about two thousand enzymes, and the chance of obtaining them all in a random trial is only one part in (10

^{20})^{2000}= 10^{40,000}, which is an outrageously small probability that could not be faced even if the whole universe consisted of organic soup." This is about the same chance as throwing an uninterrupted sequence of 50,000 sixes with a pair of dice.

Rather amusingly, he actually uses the example of rolling dice
in his argument: the *exact* same example we used earlier
(complete with a working demonstration) in order to show why this
kind of reasoning is incorrect.

After perusing these kinds of arguments, it becomes quite clear
that the creationist is utterly reliant upon the assumption that
organic chemistry has no rules and is therefore completely random,
even though this assumption is quite obviously false and
essentially renders the entire argument invalid. It is also clear
that creationists refuse to envision ancient life as being
significantly different from modern life, which is why they always
assume that the very first life form must have been something like
a modern *E Coli* bacteria rather than a primitive
self-replicating molecule. And finally, it is clear that the
average creationist either does not know how to perform a
probability analysis or is *deliberately* deceiving his
audience.

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