Tackling Cat Gravity

Today we are going to talk about Cat Gravity. I have been working on some additions and variations to the original theory and would like to share them with you. Most importantly, I have developed a method for escaping from underneath a high-gravity cat.

First, a quick refresher on Newton's law of Universal Gravitation:
The force of gravity acts between two objects, proportional to their masses and the distance between them. So, the closer two objects are to each other, the more they pull on each other. Also, a bigger object "pulls more" than a smaller object.*

*Really the force on both objects is the same, but the equal force has a larger effect on the smaller object due to its smaller mass and this makes it seem like bigger objects pull harder than smaller ones.

For example, the Earth and the moon both exert an equal gravitational force on each other. The Earth is quite a bit bigger than the moon, so the pull of gravity from the Earth causes the moon to stay in orbit around the Earth. However, the moon pulls with equal force on the Earth, but the force isn't big enough to move the entire Earth. Instead we get ocean tides due to the pull of the moon's gravity.

So, applying this to a case of cat gravity, you see that the cat gravity pulls on the Earth as well as on any human that may be trapped by the cat. The increased gravity in the cat is not only acting downward towards the Earth, but also pulling on any object near the cat. The closer the cat is to you, the stronger the gravitational force. This may explain why you often find yourself unexpected trapped by a cat. "Wait, how did this happen?" you may ask yourself when you look down and discover you are now stuck by cat gravity. Well, the attractive gravitational forces between you and the cat pulled the cat unto your lap. One must be very diligent in order to successfully fight against the powers of cat gravity.

Thankfully, cat gravity is weaker than the Earth's gravity. If this were not the case, then all sorts of objects would be attracted to the cat when its gravity levels are high. Luckily the combination of the Earth's gravitational pull and friction generally keep household objects in place. Also, when trapped with a cat on your lap, you may have noticed that the cat doesn't FEEL significantly heavier than you expect. This is because the added force of cat gravity acting downward to the Earth isn't the main force at play here.* The bigger issue you must overcome is the gravitational force between you and the cat.

*Note: This theory is vastly different than Wood's original theory of cat gravity and represents an alternative theory to the original. Much work is required before we know for sure which theory better explains the phenomenon of cat gravity.

When a cat is on your lap, the distance between the two objects (you and the cat) is basically zero, meaning that the force between you and the cat is nearly infinite.* THIS is why it is so hard for you to remove the cat from your own lap. Additionally, the cat sneakily traps you in a way that you must depend on arm strength alone to overcome the force between you and the cat. This same attractive force between you and the cat is also what makes it so difficult to stand up. The attractive force is so strong that you have effectively become one object with a new mass distribution. With a cat stuck to your lap by a nearly infinite force of cat gravity, your center of mass is suddenly significantly skewed towards your thighs. Of COURSE its hard to stand up with all this mass in the "wrong" place.

*Note: The gravitational constant of cat gravity is much smaller than that of the Earth's gravity. Also, the mass of the Earth is much MUCH larger than the mass of a human. This explains why the Earth's gravity can overcome even "nearly infinite" forces of cat gravity acting between a cat and a human. The Earth's gravitational force acting on the cat is always larger than even the strongest force of cat gravity acting between cat and human. This is why cats like laps so much. In this position, the human is trapped by both the cat-gravity-center-of-mass-skew phenomena described above and the Earth's gravity pulling down on both cat and human. 

This also helps to explain why it is so hard for you to remove a cat from your own lap, but it is quite easy for someone else to remove a cat from your lap (or for you to remove a cat from someone else's lap.) When the second person gets close enough, cat gravity attracts the cat to both people. This force pulling in the opposite direction reduces the force that must be applied to the cat in order to remove it from Person A (who was previously trapped-by-cat.) Person B is able to make use of physical strength other than arm-only dead lifting, plus make use of the attractive force now pulling the cat towards themselves. You can now see why Person B can easily remove the cat. Person B then has the option to either keep the cat or make use of the Earth's stronger gravitational force in order to put down the cat. Because the Earth's gravity is so much stronger than cat gravity, it can be used to assist in removing the cat. As you will see later, this is the key to escaping cat gravity.

Before we discuss the escape method, we must discuss an important safety issue. There is a serious risk involved with overcoming the force of cat gravity between you and the cat. Improper cat removal often leads to Claw Related Injuries (CRIes.) If you are strong enough to simply stand up with your new skewed center of mass, then the Earth's gravity will take over and pull the cat to the floor. This is known as the "Brute Strength Stand Up (BSSU) method. If you can achieve BSSU, you will be free. However, the cat will be confused and will struggle to know up from down. The nearly infinite force of cat gravity pulling the cat to you will still be acting on the cat until the Earth's gravity takes over and when the Earth's gravity does take over, the cat will want to stay attached to you instead of being pulled to the ground. In its fear and confusion of what is happening, it is likely to try to cling to you, often leading to a CRI. CRIes can range from minor scratches to more severe scratches requiring medical attention. Therefore, it is best to avoid CRIes whenever possible.

You might also be tempted to make use of the Earth's gravitational force by "dumping" the cat onto the couch next to you via twisting your lap one way or the other, also called a "Tilt and Dump" (T&D.) The TDE's experimentation in this area found that the strength of the force of cat gravity acting between human and cat requires that the lap angle to be tilted to near 90 degrees before the Earth's gravity begins to have any effect at all. This then creates a situation very similar to the BSSU method described above. The risk of CRIes is too high in both BSSU and T&D for either to be a viable solution to trapped-by-cat situations. Additionally, a T&D often leads to immediate cat gravity re-attraction due to the small distance between cat and human after a T&D. For these reasons, neither the T&D method or the BSSU method are recommended.

So, HOW do you escape cat gravity?

First of all, this escape should only be used when absolutely required. If the cat learns this behavior, it will be able to suspect your escape ahead of time. The pull of cat gravity is emotional as well as physical. The cat will not want to leave your lap. Once your cat has learned the escape behavior well enough to predict what is coming, it could lead to panic and unexpected CRIes. This means that if you are in a trapped-by-cat situation while another person is in the room with you, that person is still obligated by the rules of cat gravity to bring you food and drink. For safety reasons, you must only use this method when no other option is available to you.

The TDE's Cat Gravity Escape Method consists of 3 simple steps:
1) Pull the cat towards your center of mass
2) Stand up
3) Use the force of the Earth's gravity to set the cat down


Please note that I have never claimed this to be an "easy" way to escape cat gravity. There is NO easy way to do this. I believe the emotional pull of cat gravity plays a large role in the difficulty. All three steps described above are quite difficult to execute both emotionally and physically. However, the TDE's experimentation has found this to be a reliable and safe method when escape is required. Below is a more detailed explanation including pictures so you can better understand the process.

The Tap Dancing Engineer's Cat Gravity Escape Method:
First, prepare yourself emotionally. Is this escape absolutely required? Is there any way to avoid it? If not, take a deep breath and get ready. Some people find it helpful to apologize to the cat just prior to escaping. The TDE recommends that this only be used when first learning the escape method. If overused, it could cause the cat to learn and predict the escape method more quickly. We have already discussed the dangers associated with the cat predicting your escape.

Next, prepare yourself physically. Sit up straight. Take another deep breath (but not too obviously as we don't want the cat to learn this predictive behavior either.) Free both arms and remove any nearby objects that could be an obstacle. Move slowly as you do not want to startle the cat.

Execute step one: Using both arms, lean over and envelop the cat (placing your hands near your knees) and slide (or rotate) it upwards towards your torso. Because you are not breaking the connection between you and the cat, you should be able to slide the cat along your body. Think of two magnets that are difficult to separate, but can slide along each other. The force of gravity between you and the cat is similar. Again, this will be challenging, but much easier than trying to actually remove the cat from your body.

Once you have pulled the cat towards your torso, pause. Depending on the personality of your cat, you may want to spend 30 seconds to a minute here. For some cats, this disruption will be enough to cause them to leave on their own. However, these cats are generally not high-gravity cats. People who often find themselves in severe trapped-by-cat situations typically have higher-gravity cats who will not walk off at this point in the process. What you do in this pause depends on your cat. The TDE has found snuggling the cat at this point leads to a false sense of security and helps to avoid CRIes.


Execute step two: Keep your arms wrapped around the cat, holding it near your stomach with your arms below/around the cat. Move yourself forward to the edge of your seat slowly and carefully. Take a deep breath in and then exhale while you stand up. This is probably the most difficult step in the process. Take your time. Do not attempt to stand up until you are sure you are ready. Failure at this point may lead to you and the cat tumbling to the ground or to a startled cat and CRIes.

Again, depending on your cat, you may want to pause here. The TDE's cat can again be lulled into a false sense of security by some snuggling. The pauses also help the keep the cat from learning the steps of the process too quickly.


Finally, execute step three: This step will feel easy after completing step two, but it is still difficult. You should vary how you set the cat down to prevent it from learning the behavior too quickly. Sometimes turn and set the cat down where you were sitting. Other times, you should walk a few steps and set the cat down on the ground or on a different piece of furniture. You may want to put the cat in a cat bed or near a favorite toy to distract it from what is happening. Sometimes you should walk around holding the cat for a minute or two before setting it down. The hardest part of this step will be pulling the cat away from your body (breaking the cat gravity bond between you and the cat.) However, this is easier than trying to lift the cat while seated as the Earth's gravity will be assisting instead of hindering the process. Once you have added some amount of distance between you and the cat, the Earth's gravity will take over and it should be relatively easy to set down the cat.


As soon as you have set down the cat, move yourself away from the cat. Walk briskly, but do not run. You need to increase the distance between you and the cat quickly to prevent cat gravity re-attachment. However, running away will startle the cat and the key to this method is keeping the cat calm and unaware of what is happening until its over. So, set down the cat and move away quickly but calmly.

Once away from the cat, take some time to recover mentally and physically from your ordeal. This is not easy, but is sometimes necessary. Enjoy your freedom while it lasts!

Yes there IS a Halloween blanket on my couch. In January.

And yes, that IS an Albert Einstein doll and a clock with the first 12 elements on it. Because science!

The TDE would like to thank Wood et al. for their original work on cat gravity. Without this work, my above theories and methods would not be possible. If you'd like to learn more about their work, there's a book.

Three Awesome Content Creators and One Amazing Book

I recently finished a book that I got for Christmas. I want to tell you about it. And then you should go read it.

The book is Wild Ones by Jon Mooallem.

I first heard about this book on the podcast 99% Invisible. If you're not listening to this podcast, you should be. Technically it's about architecture and design. It's also about many many other things and is almost always fascinating. It is good.

The episode about Wild Ones is actually some audio recordings from a live show featuring Jon Mooallem reading parts of the book with musical accompaniment from the band Black Prairie. As usual, it is fascinating. I wanted to hear more of the story, so I added the book to my Christmas list.

What is the book about? It's so hard to put into words. The subtitle of the book does a whole lot better than I can. It reads "A sometimes dismaying, weirdly reassuring story about looking at people looking at animals in America." Yeah, it's pretty cumbersome as far as subtitles go, but it's the most concise description I've found.

The author digs into the story of 3 different endangered species: Polar Bears, Lange's Metalmark Butterflies, and Whooping Cranes. All of it is underlined by the fact that he is doing this as a way to be sure his young daughter is able to experience these animals before we loose them entirely. However, the story is actually about humans more than any of the 3 species he investigates. He meets conservationists and scientists working to save each of these species. He gets insider info on what we are doing for each one. He goes out in the field to watch and help. He summarizes his thoughts and feelings beautifully. You are left feeling simultaneously hopeless and hopeful somehow. You question everything we are doing yet feel motivated to do more to save our planet and endangered species. I can't explain it. It is "a sometimes dismaying, weirdly reassuring story about looking at people looking at animals in America."

The book also talks a lot about shifting baselines. Its a great way to start to understand the concept of shifting baselines. Its a common theme that he comes back to multiple times. I pondered if I should do a post about shifting baselines next week, but I think you should just read Wild Ones instead. Oh, and you can also learn a bunch about Hornaday who has also been featured on The Brain Scoop recently. I think Emily and Jon would get along great. Jon should probably be a guest on The Brain Scoop. That would be some YouTube magic. And if you're not watching The Brain Scoop, stop reading my blog and go watch it right now. Seriously.

I learned quite a bit from this book. I think that's the best part. It is nonfiction, but written in story form. You feel like the author is talking to you directly, just telling you this fascinating story of his experiences. He weaves science into it without it being awkward or dry. You gobble up the story and end up with a bunch of new knowledge. Just go read it! 

And, if you're not convinced yet, I want to share a few quotes from the book that I found particularly profound. They probably won't resonate as much out of context, but they are still good.

From Section 1: Bears
"We seem to be forever oscillating between demonizing and eradicating certain animals, and then, having beaten those creatures back, empathizing with them as underdogs and wanting to show them compassion. We exert our power, but are then unsettled by how powerful we are."

From Section 2: Butterlies
"It was a tragedy of charisma. Because of their affection for a single celebrity whale, hordes of people had jeopardized an entire species of anonymous butterfly. They weren't an angry mob; they were a loving mob. But they loved only certain things."

Discussing the "soup stage" of butterfly metamorphosis (which was also recently discussed on the Brain Scoop!):
"'You're not what you were before' Jana told me, 'but neither are you what you're going to be. The soup stage really sucks, but you have to embrace being soup for a while.'"

From Section 3: Birds
Quoting Joan McIntyre while discussing how she survives in a world she finds so discouraging.
"'You try to stay virtuous in your immediate life, you try to be correct - because you only feed the monster if you interfere too much.'"

And, I shall leave you with one last tidbit. Billy Possum: the Teddy Bear's underachieving little brother. To make sense of that statement you'll have to either read Wild Ones or listen to the 99% Invisible episode about it. Then go watch some Brain Scoop.

PS: Sorry for the lack of visuals this week. If things go as planned, I will more than make up for it next week.

Graph Fraud

Happy Wednesday BlogLand!

There are actually a bunch of things I want to post about today, but I'm going to save some of it for a later post.

First of all, go check out this article about clave and percussion rhythms. It is SO cool! I couldn't justify dedicating a whole post to this one awesome article, but I can at least throw it in at the beginning of this one. I absolutely LOVE this sort of stuff that combines math with arts and culture. Plus, it has awesome visual representations AND its about the clave which I also LOVE. This might be the best thing ever. Go read it!

OK, on to the real post. We have to talk about a serious problem plaguing the world of science. Graph Fraud! Even if your data are accurate, there is still so much manipulation that can occur in how you choose to represent that data. Often, we can let our biases and expectations to get in the way of presenting clear data. Here's a fun little story as an example.

In the spring of 2013, the Tap Dancing Engineer went to visit some of her Canadian Tap friends. Recently, the weather had been strangely warm (or maybe it had been strangely cold, I don't recall.) The TDE and the Canadian Tap Dancers hit a conversational stumble. You see, we Americans simply refuse to convert to the logical system of measurement that just about everyone else in the world uses. This system of measurement includes using Celsius instead of Fahrenheit. The Canadians have little reason to learn our system of measuring temperature (only motivation is for small talk with Americans) and vice versa. So suddenly we could not fully communicate and I could not understand exactly HOW unusually warm (or maybe cold) it had been there.

Being the scientist that I am, I of course know that to convert form C to F you simply multiply by 1.8 and then add 32. I generally do this in my head by A) multiply by 2 B) Divide that number by 10 (giving me 0.2 of the original number) C) Subtract B from A and finally D) Add 32. Its a fairly involved process and all of us spaced out for a minute to try to do the conversion in our head. This absurdity made us talk about how to quickly convert between the two numbers.

Canadian Tap Dancer Kylie said "Oh, I usually just multiply by 2 and add 30." Could it be that simple? We did the math both ways and they were pretty close. Something like 72F vs 74F (yep, must have been unusually warm.) As soon as I had access to a computer and a spreadsheet I quickly made a graph to be able to understand the full picture visually. Here is what I found:

WOAH! They are almost identical! Amazing! Its so simple and I can't believe I've been doing all that mental math for all these years. 

...

.......

............

Or, actually, it might have gone something more like this:

Canadian Tap Dancer Kylie said "Oh, I usually just multiply by 2 and add 30." Could it be that simple? We did the math both ways and they were pretty close. Something like 72F vs 74F (yep, must have been unusually warm.) As soon as I had access to a computer and a spreadsheet I quickly made a graph to be able to understand the full picture visually. Here is what I found:

Well, its pretty good at lower temperatures, but its WAY off at higher temps and its just getting worse and worse as it goes. I don't know. Maybe I do need to do all that math. Dang.

So, which one is the "real" data? They BOTH are. Yep, its true.

Whats the difference here? The ranges used for both the x axis and the y axis. First, lets look at the x axis. In the first graph it only includes 0C to 20C (32F to 68F.) Where I live, that covers a pretty large part of the year, but definitely leaves out some critical data. Now look at the second graph. It's x axis range is 10C to 60C (50F to 140F.) If we're discussing the weather, then this graph isn't even applicable. It leaves off way too much on the cold side and, for the US and Canada at least, goes too high on the hot side.

Now, lets look at the y axis. Notice how the first graph has "white space" above and below the highest and lowest points on the curves. The y axis range has been increased in order to reduce the appearance of the white space between the two lines and make them look more similar. In the second graph the y axis range has been reduced as much as possible without the curves disappearing off the top or bottom of the graph. This magnifies the differences (white space between the curves.)

And finally, I also used a different line thickness for the two graphs. By using a 3pt line thickness for the first graph (as opposed to the 2pt thickness default that was used for the second graph) I was able to make the lines look even more "on top of each other." 

So, always check your ranges. Are they reasonable for the data that is being represented? Is there a bunch of unnecessary white space on the graph? If you are comparing two (or more) graphs ALWAYS be sure to check that the axis ranges are the same. You cannot compare graphs with different ranges! And, if you ever see an article making a point with graphs that have different ranges, that is a very good sign that the author is not a good scientist and is presenting data that is skewed to support their cause. It may not be intentionally skewed and even if you unskew it, it may still support their cause, but you should still take all information from that source lightly and skeptically. 

So, lets unskew this data! I did some quick Wikipedia searching for average January temps in various Canadian cities (~-20C) as well as the Ave July high in various Arizona cities (~110F.) I have decided to set my x axis range to be -20C to 45C and set my y axis range to -20 to 130 (this is 10 degrees F outside of my lowest and highest points.) I also left all line thicknesses at the default of 2pt. Here's the graph I made:

Oh good! They are pretty darn similar. Not exactly the same, but close enough. Right?

Or ARE they?

I think its pretty obvious what I did here right? The only difference is the size and shape of the two graphs, but they look really different. Now imagine you are writing a scientific article and trying to fit a graph in without messing up the formatting of the rest of the paper. You want badly for that graph to fit in the small amount of white space you have for it. If you are trying to show that these two lines are the same, you probably wouldn't think twice about smushing it down to be only a few lines of text high. If however, you were trying to show that these two lines are different, you wouldn't dare smush your graph and risk reducing the effect of your visual. You'd probably just make it HUGE and dedicate a whole page to it instead. See how easy it can be to unintentionally skew your visuals? 

The take away? Pay attention. Especially pay attention when multiple graphs are being used to tell a story. Good scientists with solid data don't need to resort to these tricks. Good scientists with less than solid data will still present that data properly even if it means their visuals are not as clear as they'd like it to be.

Finally, a quick little note about how far off the Kylie conversion is from the actual conversion. By simple subtraction we see that for every 5 degrees C, the Kylie conversion gains 1 degree F in error. 

So there you have it. For general weather use, you'll probably be off by less than 5 degrees F. If you live someplace like Arizona or Minnesota, use the Kylie conversion with caution during extreme temperatures. Also note that using the Kylie conversion at very high and very low temps just makes you exaggerate the extremes. If its -22 and you claim it to be "about -30" I still see that and think "so its really REALLY cold. Same for the hot side. People that do not live in areas that regularly experience these weather extremes have a hard time wrapping their heads around what -22 or 110 feels like so being off by a few degrees doesn't change the conversation that much if you're talking to someone from outside of your area. To be safe, however, you should probably shave a few degrees off your statements at extreme temps (perhaps say "Its about -25." and then you're not off by much at all.)

Hopefully none of us will have to deal with temperatures quite that cold again for a while... 

Is the Kylie conversion good enough? When discussing the weather with her Canadian friends, the TDE will be using the Kylie conversion from now on. If you're writing a research paper or lab report, you should the official conversion. 

I hope I've made you a little more aware of Graph Fraud. Together, we can help others learn to eliminate Graph Fraud from their lives. Pass it on!

This Post is Not About Autism

Today we are going to talk about Correlation vs Causation!

Pssst! I've got a secret! We actually already talked about this last week. 

After talking about understanding a main cause vs truly understanding the mechanism of why last week, it seemed like a great time to talk about correlation vs causation which is pretty much the same thing. Also, a friend posted this article on Facebook and I suddenly knew what I would post about this week. Here's the graph from the article:

DISCLAIMER TIME!

As the title of this post indicates, this post is NOT about Autism. Autism is a very real and very challenging disorder and I in no way intend this post to be making light of Autism. In fact, part of my motivation here is showing how Autism has become a media hype poster child to the point where other people are making fun of the correlation hype. I am using Autism for this post mainly because all these silly graphs already exist that show the point so well. Anyway, instead of focusing on all the media hype, we SHOULD be talking about ways to help those with the disorder or, teaching people not to discriminate against those with Autism. 

OK, back to the post!

The graph above shows a correlation between Organic Food Sales and Autism. I think just about everyone who sees it will think "yeah, but thats just a coincidence." I mean, organic food can't possibly CAUSE Autism right? Right?! 

Well the TDE sure doesn't think it does. It just happens to correlate. Organic food popularity just happened to increase at the same time as Autism.*

*Note: Pay very careful attention to the x axis in that graph. 1998 to 2007 (for the final data point.) Thats only 9 years. What does this graph look like if we went back to 1970? What about 1950? What about forward to 2013? Careful choice of which subset of data to put in a visual can drastically alter the conclusions drawn from it. Be skeptical people. Always skeptical. I think perhaps we'll talk about this more next week! 

However, if you saw that graph and thought "OMG! Organic food causes Autism!" here are a couple other things that cause Autism:

1) Microwave RF Exposure

                            

2) College Tuition Rates

So, how exactly is increasing college tuition causing Autism? Its not. While microwave RF or organic food could more easily be tied to Autism (logically), the point is that these graphs show only correlation. To show that organic food causes Autism, we would need to understand the mechanism for HOW this happens. 

And really, these graphs don't actually show correlation. They show two parallel trends. If they were truly showing correlation, they would look more like the graphs below.*

Autism vs Organic Food Sales (R Squared of 0.991)

Autism vs Microwave RF Exposure (R Squared of 0.984)

Autism vs College Tuition (R Squared of 0.989)

*Note: The TDE did not have access to the raw data used to create the initial graphs. The graphs above were created by the TDE using data obtained from the original graphs which included a decent amount of error/noise due to interpolation/estimation variation. The point was not absolute accuracy, but to show what a correlation graph SHOULD look like.

A true correlation graph shows the dependent variable (in this case, Autism) on the Y axis and the suspected independent variable (whatever we are trying to show is a cause of Autism) on the X axis. The R Squared shows how good the correlation is (1.0 being perfect correlation.) These examples all happen to be linear correlations which makes everything a bit nicer. And, all three of these sill hold up as strong correlations, based on R Squared values.

Another really important point is to look at the scales used for Autism. All three are different:

1) Organic Food graph shows sales in millions of dollars per year vs total individuals diagnosed with autism per year. There are multiple problems with this. First of all, this doesn't take into account population variation. This also doesn't limit cases by age of patients. Were many adults suddenly diagnosed in the years just after Autism became better understood/recognized/diagnosed? A more accurate dependent variable would be to use the percentage of a specified age range diagnosed with Autism each year. 

Of course, the whole point of making this graph was to correlate Autism to something that obviously isn't causing Autism. So, they probably used the most compelling graph instead of the most scientifically correct graph to prove their point.

2) The Microwave RF graph shows strength of signal vs Autism incidence per 10,000 children. This is a fairly accurate measurement of Autism to use. It is a percentage of a specific population (children) which will take into account population variation. "Children" could be more accurate however. Is that infant to age 18? School age children? You get the point.

Note that this graph uses two Y-scales. One for RF Exposure and one for Autism. Its not wrong, and its often the only option, but it makes it a little harder to understand quickly. 

Also note that "incidence" is different than "individuals diagnosed." Incidence includes everyone who currently has the disorder where individuals diagnosed would only be NEW cases/diagnoses that year. The TDE isn't entirely sure which is a better metric to use to show the increase in Autism. Number of diagnoses per year is probably an easier number to track and obtain and therefore more accurate, but it doesn't capture the full story in terms of what percent of the population has the disorder. Also, note that both incidence and number of diagnoses cannot take into account the fact that we have gotten significantly better at recognizing the symptoms and diagnosing the disorder over the years. This alone could account for a large portion of the increase (though it does seem that there is more to the story than just recognizing and diagnosing it more often.)

3) The College Tuition vs Autism graph shows cumulative % of college tuition cost vs the cumulative % of Autism cases. 

First of all, what does that even mean? Scroll up and look at the graph again and look at the red line that represents Autism cases. The final point (2007) shows 100% of cases. This sets the number of cases in 2007 to be 100% and then the rest of the data is given in comparison to the number of cases in 2007. In 1999 the data point is at about 5%. This means that the number of cases in 1999 was about 5% of the number of cases in 2007. The same method is used for the college tuition numbers. 

Why would they do this? Probably to put the two variables onto the same scale (percentage from 0 to 100.) Otherwise, it would be really difficult to show them on the same graph and the correlation wouldn't as clear. By converting them both to % of the highest value, we can see how the two trends are similar. 

There isn't anything terribly wrong (scientifically) with this method. However, the problem with this is that it skews the raw data and "hides" it from the readers. I didn't have the raw numbers and had to make my correlation graph with cumulative percents instead of actual number of cases and actual cost of college tuition. A much better graph would have been to graph the raw data as a true correlation graph or at least use the 2 axis method like in the RF exposure graph.

I think these three graphs were intentionally made in a way that keeps the chronological year on the X-axis. We like seeing how things increase over time. It makes sense to us. By eliminating the time scale we loose the connection to "this is happening now, in my life." We also loose the exponential curve which is scary! (media hype) Just understand that the original graphs are not truly correlating the two variables to each other. 

Anyway, we got off on a Correlation tangent there. Back to the point.

Causation:

So, how do we show causation? Well you have to full understand the mechanism. 

Here's a simple example:

For a while, I was lactose intolerant.* Many people self-diagnose themselves with lactose intolerance via correlation. They notice that when they eat foods that contain a large amount of dairy they have digestive distress. You could also eliminate dairy from your diet entirely and then correlate the elimination of dairy to the elimination of "attacks." You could probably even correlate different amounts of milk consumed to some other variable like subjective rating of intestinal pain or maybe total length of time of the lactose "attack." You might even get a nice graph and a strong correlation. Of course, it would be awful of you to force someone that is lactose intolerant to drink milk so don't do that. You can do it to yourself if you value the scientific knowledge over your own comfort I suppose. However, this would show correlation only.

*Yes, past tense. I can eat dairy again now! Yay!

In my case, however, I had a test done. They gave me a class full of dissolved lactose and made me drink it. It wasn't very nice of them, but it was for diagnostic purposes and didn't do any lasting harm. Then, over the course of two hours they had me exhale into a bag at regular intervals. My breath was analyzed for different sugars via Gas Chromatography.* We know how lactose is broken down in the body (the enzyme Lactase breaks Lactose into glucose and galactose which we can digest more easily than Lactose.) By analyzing the sugars on my breath they were able to tell that my Lactase wasn't doing its job well enough. Because we understand the mechanism, we know what to test in order to confirm the presence or absence of Lactase, and we can therefore know for sure when someone is lactose intolerant. I'm sure there are other, more invasive, ways to directly measure Lactase also, but drinking a glass of lactose mixture was probably less painful than whatever they would have needed to do to get a sample of my gut enzymes directly.

*Of COURSE I asked what they were doing with it and how they were analyzing it!

Sure, its probably easier to just eliminate Lactose and see if you feel better, but then you only have correlation, not causation. Some people are actually allergic to milk protein as opposed to being unable to digest milk sugar. Or, maybe some people are super sensitive to the additives in milk and would be fine if they drank only organic milk. I know some people have issues drinking pasteurized milk but can handle raw milk without problems. So, just because you feel better if you eliminate all dairy doesn't mean that you Lactose Intolerant. Its only correlation, not causation. 

So, what does actually CAUSE Autism? We don't know. We have lots of correlation and lots of hype. We have lots of scientists and doctors working on it. But right now we still don't know. Sorry.

Shampoo Experiment Part 3

As promised, we'll now tie up a few loose ends on the Shampoo Experiment.

I still don't know WHY:
The original question when I started this experiment was to understand why when I don't wash my hair, I feel "as dirty" as when I don't take a shower at all. It may seem like I've answered that question, but I haven't. I still don't actually know why. Sad face. :(

What I do know are the trends from my data. What I learned is the following:
1) A lot of it is bias and expectations and "in my head."
2) The use of water has the biggest effect on dirtiness rating, with a smaller effect from shampoo.
3) The use of leave-in conditioner has the largest effect on head smell and seemed to be the only clear and consistent correlation for head smell.

From this information I was able to make an overall conclusion that if I want to avoid feeling dirty and avoid being a smelly-head, then I should get my hair wet and use my leave-in conditioner. So, I can avoid using shampoo every day, but it seems that I do need to get my hair wet every day.

So, why do I feel dirty when I don't wash my hair? Because I didn't get my hair wet.

Yes, but WHY does getting my hair wet make me feel clean? I don't know the full mechanism. Is it that water alone is cleansing enough to avoid being dirty or is it that getting my hair wet is enough to avoid making myself feel dirty because I think I'm dirty? I still don't know.

This is a rather simple case and its rather easy to understand the difference between discovering a correlation and understanding the full mechanism. In more complicated situations it rather easy to find yourself in a grey area where you think you've discovered the "why" but you really haven't. I point this out simply to get more of you to think about what you truly understand and what you don't. You don't need to know and understand the full mechanism of everything in your life. However, if you start to recognize when you do not know the true "why" then you will start to ask "why" more often, and you could even start researching/learning some of those "whys" and then you'll have turned into a scientist.* And we sure need more scientists!

*According to The World English Dictionary on dictionary.com a scientist is "a person who studies or practices any of the sciences or who uses the scientific methods." The scientific method is really just a formalized way of asking "why." So yeah, digging into the "whys" in your life makes you a scientist. I actually rather like the way it is defined on Wikipedia too. "A scientist, in a broad sense, is one engaging in a systematic activity to acquire knowledge." Thats just lovely isn't it? 

Another point I'd like to make here is that I see this as an analogy for the difference between experimental scientists and theoretical scientists. The experimental scientist just needs to know how things work. For them, "Because I didn't get my hair wet" is enough. They can use this information to create the desired outcome. They (hopefully) understand that they do not know the true why, but they don't need to acquire that knowledge. The theoretical scientist on the other hand cannot stand not knowing the full explanation. They need to find the full mechanism. They think "But WHY does water matter so much?" and it nags at them until they go figure it out.*

*This whole paragraph is purely the opinion of the TDE. This is not an official definition at ALL.

The Tap Dancing Engineer would like to thank theoretical scientists for their inability to move on from these questions. Because of this, I can get answers to some of my "whys" that I am uninterested in researching for myself. Of course, all scientists want to understand as much "why" as possible. We experimental scientists, however, just decide to move on to other questions and leave the nitpicky details to the theoretical scientists. Both are important and we support each other. And SCIENCE!


Next up, my extra trial day!
So, partially through my experiment, as I was compiling the data, I noticed the trend between leave-in conditioner and head smell. I decided to confirm this an additional way just to be sure. So, here's what I did:
Day 28: Control 1
Day 29: Body shower and dry Shampoo (so that it wasn't too visually obvious that my hair was dirtier than normal)
Day 30: Body shower only, applied leave in conditioner to dry hair
Day 31: In the morning, just before the smell rating, I sprayed a small amount of leave-in conditioner on the top of my head without my smell tester seeing this. I made sure my hair looked and felt dry just like other days and then we completed the smell test.

So, I went 2 days without shampoo or even getting my hair wet, but I applied leave-in conditioner to dry hair as a way to "mask" my head smell and test if the smell of my leave-in conditioner was the "expected" smell (given a rating of 1.)

The result? Dirty rating of 5. Man, I felt GROSS! I couldn't wait to get in the shower after the smell rating. Head smell rating was a 1. HA! SCIENCE!

So, my extra confirmation trial did in fact confirm that as long as I smell like my leave-in conditioner my head smell was rated the same as when I take a full shower. The take away here is that if I ever have to skip a shower for some reason but am worried about smelling less than fresh, I can spray on some leave in conditioner and be good to go.


Now, I know what you're thinking. Is that head smell rating even a good indication? Did you always get a rating of 1 on Control 1 days? Or, at least you SHOULD be thinking that. And the answer is that no, I didn't always get a 1 result on Control 1 days. But it's so much more interesting than you might think. Here's a list of my results for Control 1 days:

1, 1, 1, 2, 2, 1, 2
There were also multiple Control 1 days that were skipped for a variety of reasons. Sometimes we just forgot. Oops.

Anyway, what is quite interesting to me is that the first three ratings are 1. After that, the next two ratings are 2. And, these two ratings occurred while I had my period. After my period, Control 1 rating went back down to a 1. And then, well, there was a random 2 rating one day. Given that its subjective ratings on a 1-5 scale, thats pretty impressive repeatability and shows a trend related to my period. Here's a graph that illustrates it much better:

According to this data, my head smells worse (or at least different) when I have my period. Cool! Of course, more data would be better considering that the "Period Ave" is only 2 data points. However, its an interesting trend. Apparently, in some cases, a woman's menstrual cycle CAN mess everything up. Or at least make data analysis more challenging. 

And with that, I think the Shampoo Experiment is fully explained. There are many opportunities for additional experimentation. Here are some suggestions:

1) Just more data in general, and of course more blind testing to help reduce bias. Even just more smellers would be a vast improvement.

2) I know what causes ME to feel dirty and MY head to smell clean, but is this the same for everyone?

3) Additional investigation of scalp scrub as my data was less than conclusive.

4) A suggestion from reader Katherine: Some trials that involve washing hair in the sink without washing body. 

5) Armpit smell didn't seem to correlate to overall dirtiness feeling. Is there something that does correlate that would be a better (less-biased) way to look for trends?

6) Of course, there is the question of WHY does getting my hair wet make me feel clean?

And I'm sure there are many many more. Leave your suggestions in the comments. Take some of the suggestions and run your own experiment. Be sure to let me know what you learn!

Next week!

What will the TDE bring you next week? I don't know yet! You'll just have to wait and be surprised. Yay!