Wow! I forgot I even started this blogspot. It's been 5 years since I wrote the things on here. I have changed so much in last 5 years I don't even feel like the same person that posted those things. I've matured in my marriage, we've had two kids (Henry (3) and Amelia (1)) and I've started going back to school for my true calling in life, mechanical engineering.
I've had these thoughts about science and creation rattling around in my brain for awhile and it's really hard to talk about these things to people face to face especially if they are biased or not open to ideas that challenge their assumptions of the world. Today I remembered about this blogspot and thought "why not" Blobspot has become rather obsolete in the last five years and I have almost no followers so what harm could it do to throw this out into the vast web. Putting these thoughts on here will be more therapy for me then any sort of enlightenment for passers-by. Sometimes putting ideas down in writing helps me to organize, prioritize and maybe change my mind about things so that what I'm doing here. This is not a finished work but a spilling out of thoughts.
I started my back to school campaign by studying vigorously to pass the math placement exam. After 3 months of review I took the test and placed into math 1080 pre-calc, not good enough. I spent a couple more weeks reviewing and re-took the exam and I did it, got into calculus! This was critical for me because math was the thing that was preventing me from going into engineering. The confused look on the testing center workers face when she got my results, like she had never seen a score that high, was a most satisfying experience (prideful but satisfying).
A little context: I've always know I wanted to be an engineer but I always made excuses for why I never really tried. In retrospect I can see that I was afraid I didn't have what I took to get through the rigorous course work to become an engineer and honestly I didn't back then. With maturing into my late 20's and becoming a father I began to understand an important thing and I make sure tell this to anyone who will listen: "It's not about HAVING what it takes, it's about DOING whatever it takes." If you have that attitude about your goal then you do "have" what it takes. Back then when I was making excuses for not going to school for engineering I didn't have that attitude but now I do.
So, on to Calculus.. Guess what it's not that hard. Calc 2 okay, that was hard (not the greatest teacher). Calc 3 I actually enjoyed this! Then Differential Equations. Diff eq's is amaz-ballz! and this is where we start getting into what I want to talk about. On the first day of diff eq's we talked about Malthus and his population models. It starts out with the idea that the rate of population growth is proportional to the amount of people in the population, because if there are more people there are more babies being born. Mathematically dp/dt = kp where k is a constant of proportionality which is either calculated by applying initial condition or by other educated guessing. Once we do calculus magic to the equation we get P=e^kt+C where C is constant that results from the calculus magic and can simply be taken out of the exponent and put in as a new constant A so we have P=Ae^kt (btw t is time). This is the where the equation for simple interest comes from if A=1 and k=interest rate r (p=e^rt).
Okay still with me? Good! What we do now is apply initial condition or in other words put in real measured data we can solve for A and k. If we let t=0 (assuming we know the population at t=0) k goes to zero and we have a value for A. Then we can use algebra to solve for k. What you end up with is a "population model" where P = Ae^kt. The thing I need you to understand from this is that we assumed that the population growth was proportional to the population and by applying a very small set of data we came up with two constants that define what that ratio of growth to population is.
What does this have to do with science? This exact method (and in many cases exact model) is used in every branch of science to come up with equations that we use to interpolate data. Many of the "constants" that we memorize and use in equations are derived from this method or a similar method. The thing about Malthus' population model is that it was WAY off. Laughably so. Obviously we need to figure in many more considerations to be able to model population growth like, environmental capacity, cultural shifts, plagues, wars, economic factors, technology shift ect.. A revised model was made called the logistical model and it used the same principle as the one I showed you except that the beginning assumption was slightly more sophisticated assuming that the population growth is proportional to the population but tapers off as the population reaches it's maximum capacity. After years of actually population date we know the logistical equation fails miserably as well.
As I eluded to many scientific equations use this kind of thinking. Have you every heard of a half-life? A half-life is the amount of time it takes something to be reduced by half. In every case half-life uses the Malthusian model of assumed proportionality. In most cases that's fine and very useful like in biological half-life of drug/chemicals in the body. It works well in that case because we are only talking about a short amount of time and if the model is 25% off it doesn't matter. If a medical scientist tells you how long a drug will be in your system it is not usually a precise number, they will tell you "5-7 days" not "5.002342 days." But in other cases like radiocarbon dating or any other dating based on decay use half-life. Exactly like the laughable Malthus population model all decay calculations assume that the rate at which the decay occurs is proportional to the amount that is present. More radio active isotopes more decay will occur (like more people have more babies but reverse). What I always tell people to make my point about this (maybe not true but gets my point accros) "Do you think someone measured a radioactive rock and let it sit for 250 years and came back an measured it again?"
Here is the logic:
1. Assume decay is proportional and not affected by any other factors (I already don't agree)
2. Use diff eq's and a very limited set of data to define the proportionality and derive a function over time. (carbon dating was the first dating and it was invented in the 1940's so there is at MOST less then 80 years of possible data for any scientific calculation likely is much smaller for any one data set)
3. Take actual measurements and input that data into your function and out pops a time.
This last semester I just finished I took general chemistry, chemistry lab and physics 1(for scientist and engineers) and I learned A LOT of stuff. It was amazing to recognize some of the equations we were using (mostly in chemistry) as functions derived from the same kind of differential equations I just finished learning about. The course didn't provide proofs for these formulas which is fine but it bothered me knowing that these formulas are based on assumptions and the book or instructor not disclosing that and instead delivering the material as if it is the immutable truth. (I was overwhelmed by homework/exams/study so I didn't make a list of them but I will do that so people don't have to just take my word for it.)
Another separate math related issue with science: linear interpolation.
In the first assignment for chemistry lab we were given a data set taken my the teacher where she measured the diameter of a balloon with every pump of the air pump. We were asked to plot the data, create a linear approximation of the data set and use our linear approximation to interpolate what the diameter would have been at any arbitrary point, like after 3.56 pumps the diameter of the balloon was blah blah. I told the teacher that this was not accurate because the relation ship to the number of pumps to diameter is not linear. The relationship is actually logarithmic because it is always growing but the rate of growth is diminishing with time. Imagine how one pump would affect the diameter when you first started, there would be a big change. However if the balloon was enormous one pump would hardly affect the diameter at all. But this is one of the cases where the error doesn't really matter because the balloon pops after relatively short amount of pumps and we (important!)interpolated date that was within the domain of our actual data. If the teacher asked be what the diameter would be after 65 billion pumps my linear approximation would fail laughably like Malthus's population model.
Now you are about ready to understand what has been bothering me lately. There are so many people out there that think I'm an idiot for believing in God and the reason they think that is, in no small part, due the numbers thrown around by "science."
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