Category Archives: Sensitivity Analysis

5 Big Reasons Why “Global Warming Is A Fact” Is A Lie

burningearthIs man-made global warming occurring? Despite what you may read or hear from the media, man-made global warming has not been established beyond a reasonable doubt. Here are five big reasons why:

1. Many experienced and credible scientists with good character do not believe that man-made global warming has been proved.

2. Proponents of man-made global warming claim that warming is a fact because of “consensus”; i.e they say that a majority of scientists agree that man-made global warming is happening. But consensus is a logical fallacy, and a sign of junk science. There have been numerous instances where a minority of scientists have ultimately been proven correct, regardless of the prevailing consensus of the day. Science is based on fact, not on a vote of scientists.

3. Proponents of man-made global warming, if they truly believed in their research and analysis, would welcome the views of skeptics, because only by such challenges does science eventually converge on the truth. Instead, many proponents of man-made global warming do not welcome criticism or skeptical inquiry, and instead wage personal attacks on the skeptics. (Personal attacks are an example of the “ad hominem” logical fallacy.)

4. Proponents of man-made global warming base their beliefs on data that cannot be replicated by other scientists.

5. Proponents of man-made global warming are continually adjusting the “models” they previously created and used for predicting today’s weather, when today’s weather is not what was predicted by their earlier models. And rather than admit failure, the proponents try to obscure that fact by making up excuses and continually tinkering with their models.

A couple of interesting and thorough overviews of the junk science underlying the proponents of global warming can be found here (both by Robert Wagner):

Global Warming ‘Science’; What Investors Need To Know, Don’t Just Trust The “Experts

Climate ‘Science’ Bombshell May Be Getting Ready To Burst

The following recent article is also of interest:

The game is up for climate change believers” by Charles Moore.

(Be sure to check the comments at the end of the article by Exton, “Word of the Environmentalist.”)

p.s. I’m finding less time to compose in-depth posts, so am trying to provide brief updates of interesting news bites through twitter, which you can follow here:

-Ed Walker


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ET Extra: Vaccines And Autism And Fraud

Engineering Thinking Extra Is A Short Review Of A Current Hot Topic

A Major Study Purporting To Show That The MMR Vaccine Causes Autism Has Been Shown To Be A Fraud: What Can We Learn?

A 1998 Lancet paper by Dr. Andrew Wakefield et al concluded that the measles, mumps, and rubella (MMR) vaccine caused autism. As a result, many parents refused to have their children vaccinated. On January 5 of this year the British Medical Journal (BMJ) declared that the Wakefield paper was a fraud.

Scientific corruption is, in my view, a most foul betrayal of the public trust. Hopefully Dr. Wakefield and his cohorts will receive appropriate punishment. Moving on, however, what can we learn from this revelation of fraud? There are two basic cautions provided by Engineering Thinking:

1. Wakefield’s Fraud Does Not Prove That Vaccines Are Safe

Since the Wakefield paper was a fraud, does this mean vaccines do not cause autism? No, drawing that conclusion would be a logical fallacy. This may seem counter-intuitive, but here’s an example to help clarify: Assume that I published a paper purporting to show that 2 + 2 = 4. In my proof I used erroneous math and logical fallacies. Therefore my paper did not prove that 2 + 2 = 4. However, that does not alter the fact that 2 + 2 = 4.

In other words, there is nothing in the Wakefield saga that allows one to eliminate the possibility that vaccines may contribute to autism.

2. The Link To Autism May Be Multi-Variable

Humans tend to look for a smoking gun — a single cause — to explain a fearful event. If we eliminate the smoking gun, then all is okay. However, some medical doctors have expressed concern about vaccinations based on mercury content, mixing of multiple vaccines, and other issues. The hypothesis is that it is a combination of variables, related to the production and/or application of vaccines, that may overwhelm the embryonic neurological system, causing damage that manifests as autism.

There are independent studies that strongly suggest that MMR vaccines are safe, but those do not necessarily constitute a proof. Unfortunately, many studies are flawed or even completely invalid because of the improper application of statistical methods. Failure to account for significant variables as mentioned above can also contribute to poor or invalid results.

Therefore what can we conclude?

Showing the Wakefield paper to be a fraud, as explosive and damaging as it may be, does not move the science forward with regard to the key question: do vaccines, under certain conditions, contribute to autism?

If anyone has some links to studies that are scientifically sound (that use appropriate statistics and that properly account for all variables), and which demonstrate that vaccines are indeed unambiguously not a factor in autism, please forward them to me for posting here.

In the meantime, the old cliché “better safe than sorry” is best applied. Some suggestions for consideration can be found in an earlier blog: “Off Topic: The Autism Epidemic“.

-Ed Walker


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The Government’s Policies And Our Economic Crisis (Part 1)

An important engineering principle is empirical validation, or proving that something is correct by test and measurement. Theories are great, but a theory isn’t worth two cents if it doesn’t match the real world.

Let’s see how the principle of empirical validation affects an engineering team’s deliberations, as they investigate the root cause of economic downturns.

Charlie (team leader): The purpose of today’s meeting is to review the results of our research on the causes of economic meltdowns in the United States. Nigel, we’ll start with you.

Nigel: In reviewing over a dozen recessions and depressions in the U.S. economy that occurred during the last hundred years, I found that most of them were normal business cycles that lasted about one year on average. The Great Depression that started in 1929 was an exception, because it persisted for several years.

Charlie: Why was the Great Depression so much longer than the other downturns?

Nigel: By doing a sensitivity analysis of major variables, I found that the length of the downturns was strongly correlated with the degree of governmental involvement in the economy. For example, a severe depression occurred in 1920 during which the government did very little, yet the depression only lasted about eighteen months. By comparison, the government intervened massively during the ten years of the Great Depression.

Charlie: Okay, but we all know that correlation is not proof of causation; maybe the government was coincidentally involved when the economy got worse. Any other thoughts? Marcy?

Marcy: Based on Nigel’s results I searched for a prototypical experiment that would support or refute his hypothesis. Fortunately, such an experiment was conducted over several decades wherein massive governmental control of an economy was performed. The experiment was carried out by the Soviet Union during the years 1922 to 1991.

Charlie: Good; there’s nothing better than empirical evidence. And the results?

Marcy: Economic performance was persistently poor. One could easily conclude that the experiment was a striking failure.

Charlie: Could the failure be explained by some variables other than the government’s involvement?

Marcy: In my view, no. The Soviet citizens were of high intelligence, and despite the single-minded — even brutal —  management of the economy by the government, the results were dismal. In fact, considering the intensity and duration of the experiment, had it been a success the Soviet Union today would likely be the world’s dominant economic superpower.

Sam: Some economists today are saying the opposite of what Nigel and Marcy report. They’re saying that the Great Depression lasted so long because the government didn’t intervene enough; that the government should have spent even more money than it did.

Charlie: Do those economists have any empirical data to support their claims?

Sam: Um, well, they cite the fact that the depression ended following World War Two, during which the government spent a lot more money.

Charlie: A correlation, yes, but not necessarily a cause. How do they explain all of the instances where the government did little, and yet, as Nigel found, the economy quickly recovered? How do they explain the fact that the economy worsened when government spending was massively increased prior to the world war? How do they discount the very convincing experiment performed by the Soviet Union? It seems that their theory does not match the empirical evidence.

Sam: Um, well, I guess I don’t have the answer to that.

Charlie: Okay, if those economists have no empirical data to back up their theory, then we should discount it. It appears then that the best hypothesis for the root cause of extended economic meltdowns is governmental interference. All agreed? Good; meeting adjourned.

Next Post:

A Trip To The Government Store

-Ed Walker


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We Interrupt This Blog To Bring You This Important Health Care Bulletin: It’s Just A Systems Thing

The post originally scheduled for this time (“Put On Your Emotional Armor”) has been delayed so that engineering thinking can be applied to the current debate about health care reform.

Complex subjects such as health care can — and should — be reviewed and debated from various perspectives, including federalism, privacy, cost, freedom of choice, and others. But is there a way to avoid being overwhelmed by complexity? Oftentimes, yes. One of the most important tools of the engineer is called sensitivity analysis. This is simply a methodical way of separating the wheat from the chaff, or a way of detecting what is truly important and allowing trivial distractions to be dismissed.

For example, assume that you’re relaxing on a beach and are trying to optimize your comfort. You’ve picked a scenic spot with a breeze, you’ve adjusted the umbrella for just the right amount of shade while not spoiling the view, and you’ve brought along your favorite drink. As you take a sip, you gaze out idly at the blue-green sea, and —- no, it can’t be! Suddenly there is only one factor of importance affecting your comfort, and you make a frantic dash away from the shore to escape the inrushing monster tidal wave.

These dominant factors are the Big Gorilla variables. Compared to the Big Gorilla, all other factors are largely irrelevant, and one can simply focus on the Big Gorilla and see where it leads. From an engineering thinking perspective, the Big Gorilla variable in the health care debate is how well government performs as a system:

Assertion: The government is a better system for providing health care services to the public than the free market.

Assumption: “Government” is defined as a social system wherein power is centralized in the hands of a relatively small number of leaders who make decisions for the balance of the population. “Free Market” is defined as a social system wherein power is distributed broadly and evenly among the population.


  1. If a social system is effective at providing health care services then there will be a tendency for good outcomes, regardless of the talent of those who work within the system.
  2. If a social system is ineffective at providing health care services then there will be a tendency for poor outcomes, regardless of the talent of those who work within the system.
  3. The governmental system has the following attributes: (Note: these attributes will be explained in more depth in future posts.)
    1. Poor feedback (services provided are not significantly affected by whether or not the population wants or likes the services).
    2. Slow response time (it responds very sluggishly, which can trigger instabilities that can create the opposite of the desired result).
    3. Control is not distributed (its centralized organization makes it unreliable, inefficient, and prone to corruption).
  4. The free market system has attributes that are the opposite of the governmental system.

Conclusion: The assertion is false. Government provision of health care services will be ineffective and therefore highly unsatisfactory compared to the free-market alternative.

Corollary: Improvement in health care services will be more effectively achieved with the government strictly avoiding any attempt to control the health care industry, and instead by providing a judicial framework that supports free-market principles (e.g. prevention of monopolies, elimination of laws that favor one company over another, reduction of barriers to free trade between the states, and streamlining judicial review rather than relying on regulatory micromanagement).

An important aspect of analysis is that it often allows results to be generalized. The above analysis leads to the following important general conclusion:

The Government Should be Used

To The Minimum Extent Possible For Providing Services

The above may seem counterintuitive since, after all, what is government for, if not for providing services? Nonetheless, an objective systems analysis indicates that the governmental system is vastly inferior to the alternative of free-market capitalism. This is not an ideological conclusion, nor is it a criticism of government workers. It is simply a fact that, regardless of the talent, integrity, intentions, and compassion of those in government, they will always be hobbled by an inefficient system. Plus some of these good folks will, over time, tend to become corrupt.

Engineering thinking requires, wherever possible, that theory be compared to empirical data. A future post will present a Big Gorilla historical antecedent that supports the above conclusion.

Next post:

We Return To Our Regular Scheduled Program: Put On Your Emotional Armor

-Ed Walker