The Bones

When the bones look good, the facts don't matter.

When it comes right down to it, the theory of evolution is based on nothing more than appearance. Men and apes presumably evolved from a common ancestor just because their bones look similar.

Before DNA analysis, the entire mythical tree of animal life was based entirely on physical similarity of bones. DNA analysis was supposed to confirm and quantify the similarity—but it didn’t. DNA analysis gave different results from what the paleontologists believed based on the bones; and even gave different results depending upon which genes or proteins were analyzed.

Similarity is Subjective

The goal of this month’s newsletter is to show that the theory of evolution is based on nothing more than similarities, which are subjective. Some evolutionary conclusions are based on similarity of bones or fossils. Some evolutionary conclusions are based on similarity of DNA, or proteins, or other organic molecules. Some evolutionary conclusions are even base on similarity of behavioral characteristics. Fundamentally, the theory of evolution is based on the observation that living things are similar, to a greater or lesser extent. It isn’t based on the observation of one kind of living thing evolving into another kind of living thing because that has never been observed in nature, or in the laboratory. The theory of evolution depends entirely upon the supposition that living things are similar because they have a common ancestor.

There are two problems with this. First, the assumption that there is a common ancestor is false. If you believe that, there might not be anything anyone can say to make you change your mind. So, let’s not even go there.

The second problem is that similarity is in the eye of the beholder. We can’t prove that to you—but you can prove it to yourself experimentally.

Learn by Playing

You learn more from experience than you do from anything anyone tells you. You gain experience by playing.

Most of what I learned, I learned from personal experience. What I learned from personal experience has been much more reliable than what I’ve been told by people without any real experimental experience.

In the 1950’s, kids learned science by playing. Most of our toys were science-based. We played with gyroscopes and bounced balls.

We built structures from blocks, Lincoln logs, American Bricks (the forerunners of Lego), Tinker Toys, and Erector Sets. We learned as much from the towers that fell down as we did from the towers that remained standing.

In the 1950’s, cereal boxes had cheap science toys in them, including plastic frogmen and submarines which used baking soda to dive and return to the surface in our bathtubs. One even had a tiny tin boat which used a very short birthday candle to boil a couple of drops of water which produced the steam to putt-putt along for a few seconds. (It didn’t work very well, but it taught us about steam expansion.)

We launched hollow plastic rocket ships which were filled with water and air compressed by a bicycle pump. They were held to the launch pad until a restraint was released. When filled mostly with air, there wasn’t enough water to be pushed down to cause conservation of momentum to make the rocket go up. When filled mostly with water, there wasn’t enough compressed air to push the water out. It flew the highest when we put in the right ratio of air and water. We learned how to make it fly the highest through trial-and-error (that is, experimentation).

As children, we learned about conservation of momentum and inelastic collisions from marbles and pool tables, and learned how to knock pins into each other at the bowling alley to pick up a spare. We learned to make ping pong balls curve and take funny bounces by spinning them.

We learned about electric motors, friction, gear ratios, and centrifugal force tinkering with the cars we raced at the slot car track.

I used to “watch Mr. Wizard,” and was disappointed several Christmas mornings until my parents finally decided I was old enough for a chemistry set. I built an oscilloscope and a color TV from a Heathkit in 1961.

The point is, in those days, we already understood science before we learned about it in school because we had learned science by playing with things. All we learned at school was how to do the calculations to quantify what we already knew would happen.

Play First

Learning from experience never stops. I was fortunate enough to be starting my career when the Motorola 6800 microprocessor was invented. There was nobody to teach me how to program it, so I just played with it. In 1975, I spent my two-week vacation writing the Space Ping Pong game, which came in fifth in “The World’s First Microprocessor Design Contest” sponsored by Motorola and EDN magazine. Do-While Jones became famous as one of the first writers about microprocessor programming simply because there weren’t any microprocessors to program before then, so nobody else had written about their experiences. It was an accident of timing. I learned by playing with them, and wrote about what I learned from experience. That experience helped me at work, too.

I learned as much from the programs that did and didn’t work as I did from the towers of blocks that did and didn’t fall down when I was a young child. I was controversial because what I learned from experience was different from what the ivory-tower computer science professors (who based their lectures on their expert academic analysis rather than actual experience) said.

A few years later, the Ada programming language was invented. I was one of the first to buy an Ada compiler and play with it. In the late 1980’s, Do-While Jones was the most published Ada programmer because I wrote about useful techniques I discovered from experience.

In the 1990’s, computer programming evolved into software development. Programming is as different from software development as gardening is from farming. They are the same activities, but on a much different scale. It is foolish to use a shovel to plant 40 acres of corn, and it is foolish to use a tractor to plant tomatoes in your backyard. Size matters. The computer science professors who had never managed a large software project were teaching students techniques which looked good on paper, but led to spectacular disasters in practice. Popular computer magazines were happy to pay me to write about how to do it right, and my readers were glad to read and try my advice because software development was “in crisis” in those days.

The best thing Bill Gates did was to drop out of college before he was misled by his teachers. He learned by teaching computers to play tic-tac-toe and count cars.

The point of all these examples is that you can trust the things you learn by doing, and you can’t always trust what people tell you. You should listen to what other people say; but then you must validate their claims experimentally.

The only part of the theory of evolution that is based on experience is breeding. What we have learned from the Kentucky Derby,¹ and the various varieties of corn and roses, is that there is a limit to how much a species can evolve. You can’t breed a fruit fly into a butterfly. Every experimental attempt to prove Darwin’s explanation for the existence of diverse forms of life on Earth has failed.

Case in Point

With all that general background, here’s a specific point: Last month’s feature article ² was about the brain and what it has to do to process information—and it was all talk. To appreciate what it takes to process information, you have to try to do it. That’s what we hope you will do this month.

Last month’s newsletter described how the TREE-SORT card system used a tree sort to sort through data to identify a particular tree. This month’s newsletter includes the link below ³ to a small database so you can really appreciate the processing power of the brain.

Younger readers probably know how to write an app for a phone. Here’s a science fair project for you. Write an app so that someone walking in the woods who spots a tree, can pull out his phone, answer some questions about leaves, needles, seeds, and find out what kind of tree it is.

Once you know how to make the app work with the small database of 36 trees we’ve given you, you could make a marketable product by increasing the database to 260 (or more) trees. Once you optimize your app, you could make a more useful product by having it search a database of symptoms and illnesses. (From a legal point of view, it might be wise to license your decision app to medical professionals who develop the database to try to shield yourself from malpractice lawsuits.)

But even if you don’t know how to write an app, it is useful to do the exercise on paper. For example, start with a list of all 36 trees written down on paper. Pick a tree to identify. If the tree you are trying to identify has needles, then scratch out all the trees that don’t have needles, leaving you with a shorter list. If the tree you are trying to identify has pinecones, scratch off all the trees that don’t have pinecones, and the list will get shorter. Keep scratching trees off the list until you get down to the tree you want to identify.

As you do this, you will recognize that the first question you ask should not be, “Does it have fan-shaped leaves?” because that only eliminates one rare tree. If it doesn’t have needles, don’t bother to ask about pinecones. Choose the questions wisely, asking questions which eliminate about half of the remaining trees each time to get to the solution the quickest.

Don’t just believe us when we say that mental processing is too complicated to have happened by chance. Experiment with the tree data identification problem yourself to learn from experience what is involved.

Similarity Bias

We claim bias is inherent in any comparison of similarity. We don’t want you to believe us just because we say so. We want you to do an experiment to prove it to yourself. We want you to use the data about trees to try to determine which trees are the most alike. No matter how objective you try to be, your objectivity will be influenced by your preconceived ideas about which trees are most similar. Please, do the experiment, and you will convince yourself that what we say is true.

Clearly, an oak tree is more like a maple tree than a cactus—but exactly how much more similar is it? Try to come up with a method that quantifies the magnitude of the differences in trees. What if your method of comparison shows that an oak tree is more like a cactus than a maple? That can’t be right!

TreeDatabase.htm contains the link to a 38-page Excel spreadsheet consisting of two summary pages and a separate data page for each of 36 trees. One summary page shows the digital representation of the 43 characteristics for each tree.

At first glance, the numbers seem to make sense. The pines have similar values, and the oaks have similar values. But look closer. A maple is more like a cactus than a live oak. Maple minus cactus is 47,244,379,653. Live oak minus maple is 412,316,895,747. The difference between an oak and maple is 8.7 times more than the difference between an oak and a cactus. Nobody would say an oak tree is more like a cactus than a maple tree! Something is wrong!

Clearly, the bit weights are wrong. The characteristic of being an evergreen tree is worth one point, and the characteristic of being a deciduous tree is worth two points. That’s a really big difference, but it only contributes to 1 point when making the comparison. Having 3 needles in a cluster is worth 2^23 (which is 8,388,608), but having 5 needles is worth 2^24 (which is 16,777,216). Clearly, the difference between having 3 needles and 5 needles isn’t 8 million times more important than being evergreen or deciduous (having no needles at all).

To be fair, the bit weights were assigned based on the position on a punched card for the purpose of tree identification—not to compare the similarity of trees. So, let’s learn from the mistake. We can’t weight the characteristics simply based on a location on a punched card.

Everyone should agree the weights need to be adjusted to make the comparison more accurate. We all agree that the difference between having leaves or needles is more significant that having 3 or 5 needles—but how much more?

The devil is in the details; but you won’t appreciate just how devilish the details are until you start working with the details. The mistake evolutionists make is that they gloss over details. If you don’t worry about the details, it is easy to say, “You can determine similarity by selecting weights properly.”

If you try to come up with a method for comparing trees, you will certainly discover that you have to make some subjective decisions when it comes to how important certain characteristics are.

Do this exercise with a friend. Try to come up with a way to measure the similarity of trees. The decisions you make will probably be different from your friend’s decisions. Who is right? You will have to negotiate with your friend to come to an agreement.

How will you know you have come to the right conclusion? You will think you have solved the problem when you get an answer that “makes sense” and satisfies you. In other words, if the results confirm your prejudice, you will accept the results. If not, you will keep changing your method until you get a result that you are happy with.

This is a universal truth, not limited to you or trees. All the evolutionists’ comparisons of bones and DNA are just as subjective, no matter how sophisticated the calculations. The calculations are verified by bias.

We can’t make this point too strongly. Every “objective” measure of similarity is verified by a subjective sanity check. If the comparisons of ratios of leg bone length to leg bone diameter indicates a similarity between apes and humans, it is touted as proof of common ancestry. If the ratios indicate humans are more closely related to horses, then the ratios are ignored.

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Science Against Evolution Home Page	Back issues of Disclosure (our newsletter)
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Footnotes:

¹ Disclosure, June 1999, “The Kentucky Derby Limit”, http://scienceagainstevolution.info/v3i9f.htm
² Disclosure, March 2021, “Our Extraordinary Brains”, http://scienceagainstevolution.info/v25i6f.htm
³ http://scienceagainstevolution.info/TreeDatabase.htm