Pseudoscience vs science – former is fake, the latter is fact for vaccines

Pseudoscience vs science – the former is a belief system that uses the trappings of science without the rigorous methodologies that value evidence. The latter is an actual rational methodology to discover facts about the natural universe.

Pseudoscience is bullshit. Science is rational knowledge.

Pseudoscience is seductive to many people partially because it’s not only easy to comprehend, but also it oversimplifies the understanding of the natural universe. Pseudoscience is the basis of alternative medicine, creationism, the anti-vaccine religion, and many other “fields” that true believers try to say is science.

Pseudoscience tries to make an argument with the statement of “it’s been proven to work,” “the link is proven”, or, alternatively, they state some negative about scientifically-supported ideas. It really is appealing because it oversimplifies complex systems and ideas.

For example, alternative medicine relies on this pseudoscience by creating the illusion that medicine can be really easy if you drink this blueberry kale shake, you will have a 100% chance of avoiding all cancer. Real science-based medicine provides real clinical information about every cancer, how it can be treated, and what the real prognosis is.

Acupuncture, chiropractic, homeopathy, naturopathy, and many other “alternative medicine” beliefs are pseudoscience. They simply lack robust evidence to support their efficacy. In fact, science has failed to establish the clinical usefulness of most alternative medicine (CAM) therapies.

Because I can’t help writing about vaccines, the pseudoscience vs science discourse applies perfectly to it. Pseudoscience uses logical fallacies, anecdotes, and misinformation to make it appear there is evidence supporting the anti-vaccine beliefs. Real science has debunked the claim that “there is a proven link between vaccines and autism,” a common and rather dangerous belief of the anti-vaccine world. 

This article will explore the pseudoscience vs science debate (not really a debate) by examining what exactly makes an idea scientific (and spoiler alert, it isn’t magic), and contrary the logic of science, what makes an idea “pseudoscientific.” So sit down, grab your favorite reading beverage, because this isn’t going to be a quick internet meme.

pseudoscience vs science

What is science

Science is an evidence-based systematic analysis without inherent opinion or emotion to answer questions about the natural world. In other words, it is a method to cut through opinions and anecdotal observations, so that one can have some reasonable expectation the proposed scientific principle can work as predicted.

This can be an issue when interpreting medical or scientific studies. Science tends to be written in nuanced, carefully supported statements. They often appear to be a bit indecisive, but it’s not.

Science is not dogmatic. Pseudoscience, religion, and alternative medicine are, by definition, dogmatic.

Moreover, science is binary – either there is evidence to support a hypothesis or there is not. Let’s go back to the hypothesis that “vaccines are linked to autism.”

Scientific research, published in high-quality journals, is given much more weight as evidence in real science. Thus, if I propose the hypothesis that “vaccines are not linked to autism,” it is supported by a boatload of powerful evidence. On the other hand, the alternate hypothesis,” vaccines are linked to autism,” is not supported by any credible, peer-reviewed, high-quality published papers.  

But all vaccine scientists are open-minded to the potential that evidence could be presented that establishes a link between vaccines and autism. But it cannot be done through a “vaccine debate,” it only can happen with real evidence. 

When a pseudoscience supporter states that “it has been proven,” (see Note 1) one must ask, “where is the evidence?”  What is more troubling is that someone who believes in this pseudoscience, such as vaccines cause autism, cannot imagine that they are wrong. Ironically, those of us who study real science almost always assume that the conclusions could be shown to be false with more evidence.

Whenever I hear that a scientist says, “we were wrong, it doesn’t work,” my response is “excellent, good science.” Pseudoscience never admits its wrong, so the pseudoscientist can claim “science isn’t perfect, so it can’t be trusted.”

Lucky for us, science works despite the various tropes of the pseudoscience world. 

Pseudoscience vs science – the scientific method

The other reason that a vaccine debate has no meaning. It’s the underlying principle of science, the scientific method.

The scientific method is an unbiased systematic approach to answer questions about the natural world, including medicine. People tend to think “science” is some magical way to explain things run by magicians called “scientists.” But real science is a rational methodology to uncover facts about the natural universe.

We didn’t conclude that vaccines are safe and effective because Paul Offit said it was so. It’s because he utilized mountains of evidence to come to that conclusion. And I mean a literal mountain (OK, maybe a small mountain) of evidence that supports the safety and effectiveness of vaccines.

The scientific method has several basic steps:

  1. Define the question – this could be anything from “does this compound have an effect on this disease?” or “how does this disease progress?”
  2. Observations – this is the subjective part of science. Do we observe trends or anomalies? Does a physician notice that every patient from a town or neighborhood exhibits the same disease? A lot of science arises from observations of the natural world, and yes, some of those observations can be anecdotes or personal observations. For example, one of the most famous stories in the early history of medicine is when Edward Jenner observed that milkmaids rarely were infected by smallpox because they were exposed to cowpox, a less virulent disease.
  3. Hypothesis – using the observations, create a hypothesis that can be tested. In Jenner’s case, he hypothesized that exposure to cowpox immunized individuals to smallpox.
  4. Experiment – simply, the scientist then tests the hypothesis with experiments and collects the data. The experiments are not designed to solely validate the hypothesis but may also attempt to refute it. In real science, attempting to nullify one’s own hypothesis is an honorable pursuit.
  5. Analyze – examining the results carefully, usually using acceptable statistical methods to determine if the hypothesis was supported or not.
  6. Interpret – sometimes the data leads to a revision of the hypothesis, which means the scientist has to return to steps 3-6. Or it confirms or supports the hypothesis, which means the researcher can move to Step 7.
  7. Publish – in today’s scientific community, scientific data and analysis is subject to the scrutiny of other scientists before it can be published, a process called “peer-review.” This is a critical step that ensures that the results can stand up to criticism of others.
  8. Retesting – many times the research is repeated by others, or the hypothesis may be slightly revised with additional data. Science is not static, it constantly revises theories as more data is gathered. For this reason alone, science is not an absolute, it is constantly seeking new data.

This is not an easy process. It requires years of research by experts who spent years of study and research getting there. And it’s really following the old instructions for shampoo – lather, rinse and repeat, several times.

To think that anyone can do this process in a couple of hours of Google research, which is definitely not the scientific method, is arrogance. And it is not the basis of an evolution debate, a vaccine debate, or a climate change debate. That’s for politics, not science.

Pseudoscience vs science – falsifiability

The ability to attempt to nullify a hypothesis (rather than just support it) through experimentation is a hallmark of real science. This is the fundamental scientific principle of falsifiability, that is, if a hypothesis is false, it can be conceivably shown by observation or experimentation – this allows scientist to have an open mind about the science of the natural world.

This is where falsification gets about confusing. You don’t actually have to falsify the hypothesis, you just have to be able to imagine or create an experiment that could. In other words, could we create an experiment that could, if successful, nullify the hypothesis? If you can, then it is science. 

Let’s look at a real-world example. Imagine that you’re going to create an experiment to support the hypothesis that all swans are white. So, you count one thousand swans over a broad area in Michigan, and you find one thousand white swans. Your conclusion isn’t “all swans are white,” but is, in fact, “in this population all swans are white.”

The falsification is if we find one non-white swan (and it’s actually a rare coloration) and can confirm that it is naturally not white, we can then state that “the majority of swans are white, except for a rare black phenotype.” So, a real scientist would think that swans are all white, but isn’t sure, and would state that the hypothesis can be nullified if we can find one non-white one.

This is why science isn’t dogmatic or absolutist. Instead, it does rely on evidence. If we had scoured the earth and could only find white swans, the hypothesis becomes harder and harder to reject.

But, and this is critical, we still can establish an experiment that will falsify our conclusion. Maybe we need to repeat the experiment over generations of swans to find the rare black swan. Maybe we are missing swans in a remote area of Canada, and we need to repeat the experiment there. The point is, we can imagine a method to falsify the hypothesis.

Keep this in mind with pseudoscience. Because it is resistant to falsifiability.

Conflating correlation and causation

Two of the most misused and misunderstood terms in evaluating scientific evidence are correlation and causation, two powerful analytical tools that are critical to evidence-based medicine and science.

Correlation is the grouping of variables that may occur together. For example, smoking correlates with lung cancer – those who smoke tend to develop lung cancer at a statistically significant higher rate than non-smokers. It’s important to note that correlation does not establish causation.

However, once you have numerous well-designed studies that correlate lung cancer to smoking, along with adding in biological and physiological models that support the correlation, then we can arrive at a consensus that not only is smoking correlated with lung cancer, it is causally related to most lung cancers.

We observe correlations every day. But they are subjective observations for which we cannot state a causal relationship without substantial research. The anti-vaccination movement is rife with these observations which they use to “prove” a cause.

An anti-vaccine conspiracy website claims that pregnant women are miscarrying babies after getting a flu vaccine. The fact is that there is a statistical chance that women will miscarry during any pregnancy irrespective of vaccination status.  This is random variability, not a cause.

In fact, based on the rate of miscarriage, we could expect that thousands of women would miscarry within 24 hours of receiving the seasonal flu shot. But it’s not correlation unless significant studies show a causal relationship.

For example, I’m also sure that thousands of people broke a bone or had a desire to eat a burger after being vaccinated, but that’s because, in a large enough population of individuals, you can find literally millions of different “adverse events” after receiving a vaccine.

So, the miscarriage rate after receiving a vaccine is not correlated.  It’s just a random observation. And there is no biological cause that could be described by anyone that would lead us to think that there is causality.

Nevertheless, the “flu vaccine causes a miscarriage” conspiracy has been thoroughly debunked by research, but still, the internet memes continue. Pseudoscience sometimes uses the same methodology (or lack of methodology) to make positive assertions without evidence.

As part of my analyses of random claims of causation or “cures,” I often use this logic to test the possibility of the medical potential of any pseudo-medicine – are there any physical, chemical or biological mechanisms that will allow it to work? If you cannot imagine it without violating some of the basic laws of science, then we should stand by Occam’s razor, which states often times the simplest solution is the best.

So, if there is no evidence of vaccinations being correlated, let alone causal, to autism, then the simplest conclusion we can make is that it isn’t related (as we have done). 

Which leads us to…

A partially acceptable way to use Google for research.

Pseudoscience vs science – establishing causality

There is actually a checklist that helps us determine when observed correlation can lead us to claim causation – may researchers use something called the Bradford Hill criteria.

English statistician Sir Austin Bradford Hill was interested in developing a set of objective criteria that could be used to provide epidemiological evidence of causality between a cause and effect. It serves as a sort of checklist for scientists who can take data that establishes correlation and then logically determine if that supports causality.

He used his criteria to establish that smoking was linked to lung cancer (and other diseases). He essentially went through each point of his criteria to show how smoking and cancer were linked.

The Bradford Hill criteria include the following points:

  1. Strength (effect size)– this is one of the important parts of this criteria – the larger the effect from the cause, the higher the probability of a causal link. This doesn’t mean small effects aren’t important, it’s just that fields like science-based medicine favor larger effects.
  2. Consistency (reproducibility) – proposed causality needs to be observed in more than just one location. Consistent data published by different researchers in different locations with different population samples strengthen the possibility that there is a link between a cause and effect.
  3. Specificity – causation requires a very specific population with a very specific disease with no other possible explanations of that causation. Again, the more specific an association is between cause and effect, the larger the possibility of a causal link.
  4. Temporality – the proposed effect must occur after the cause, and within a likely time period for which a link between cause and effect.
  5. Biological gradient – there must be some sort of dose-response effect, that is, the higher the exposure to some cause should generally lead to a higher incidence of the effect. (There are cases where a lower exposure leads to a higher incidence, so we should observe the inverse effect.)
  6. Biological plausibility – as we will discuss next, there must be a biologically plausible mechanism between cause and effect. Of course, it is possible that we lack knowledge of all aspects of a biologically plausible mechanism between some cause and effect. Even then, the potential new mechanism must not violate the basic principles of biology, chemistry, and physics.
  7. Coherence – does the proposed cause and effect fit with what we know about the disease.
  8. Experiment – does a group that lacks exposure to the effect exhibit a different outcome?

Bradford Hill developed this checklist over 50 years ago, so you could assume that there has been some evolution to the list. Some people have added one or two items to the list, like examining confounding factors and experimental bias. Those are usually evaluated in the original epidemiological research that establishes correlation, but if not, they become an unofficial part of the checklist.

These criteria should be used as a checklist. The more points that you can check off, the closer you can come to support a claim that there is a causal link between a cause and an observed effect. 

In the world of pseudoscience vs science, the former jumps right to a conclusion based on post hoc fallacies, while science takes logical steps, which are difficult and time-consuming, to reach a proper conclusion.

Hierarchy of evidence

Real science depends upon published evidence – both in quantity and in quality. This hierarchy of evidence ranges from utterly worthless sources, such as Natural News to the highest quality science represented by systematic reviews which “roll-up” data from a large number of other (mostly clinical) studies.

Cherry-picking a source to confirm your pre-ordained beliefs, without critiquing it for quality, is simply not good science. In fact, reliance on confirming your pre-established conclusions, rather than looking at the whole body of evidence, is one of the sure signs you’re engaging in pseudoscience.

The scientific consensus, the collective opinion and judgment of scientific experts in a particular field, is based on the highest quality and quantity of evidence. To deny that consensus, which pseudoscience does all the time, requires an equivalent quality and quantity of evidence – if that were out there, we would change the consensus. Clearly, pseudoscience always lacks that evidence.

The difference between real medicine and alternative medicine, such as homeopathy, chiropractic, and anti-vaccination, is that real medicine is supported by the highest quality and quantity of evidence available. It is robust and rigorously obtained. If you look at the pseudoscience medicine, you can’t find consistently rigorous evidence at the top of the hierarchy of scientific evidence.

The “pseudoscientific method”

Above, we talked about the scientific method. Next, in the pseudoscience vs science discussion, we need to list out the “pseudoscientific method.” It is the direct opposite in terms of logic and design.

  1. Use of vague, exaggerated or untestable claims.  Pseudoscience tends to present claims that are imprecise unsupported by complex scrutiny, including statistical analyses. If we look at the anti-vaccine claims, this is precisely what they do – they rarely present unbiased data, preferring anecdotes and unsubstantiated information that is, of course, much easier to digest than science.
  2. Extreme reliance on confirmation rather than refutation. Pseudoscience looks for evidence that supports its pre-ordained conclusions. Real science looks at all the evidence, including evidence that might refute the hypothesis. That’s why falsifiability is the cornerstone of good science.
  3. Lack of openness to testing by other experts. Pseudoscience researchers evade peer review before publicizing results, occasionally using press conferences to share their ideas. These pseudoscientists will claim that their ideas contradict the scientific consensus, so they must avoid the peer review process because that process is biased towards the established paradigms and consensus. They will use special pleading to claim that their results cannot arrive from the scientific method. And if they do publish their “data,” it’s almost always in low-quality, predatory journals. Or their research is retracted!
  4. Absence of progress. Pseudoscience usually fails to progress towards providing or even searching for additional evidence of its claims. We have been discussing autism and vaccines for over 20 years – science has produced nearly 150 epidemiological and clinical studies that have conclusively established that there is no link. On the other hand, pseudoscience has never produced a single peer-reviewed article that supports these beliefs. Sure, there are opinion pieces in poorly ranked journals that lack any research that meets the standards of high-quality science.
  5. Personalization of issues. Pseudoscience is often composed of closely tied social groups, and usually includes an authoritarian personality, suppression of dissent, and groupthink. In an attempt to confirm their beliefs, the group tends to identify their critics as enemies (see everything that Del Bigtree and Robert F Kennedy Jr say).
  6. Conspiracies. Pseudoscience also makes false assertions or claims of a conspiracy on the part of the scientific community to suppress results that support the pseudoscience. For example, the anti-vaccine crowd has invented numerous claims about Dr. Paul Offit in an attempt to discredit him. And any time anyone supports vaccines, they’re accused of being a Big Pharma Shill, called various racial epithets, or worse. 
  7. Use of misleading language. Pseudoscience tries to create scientific-sounding terms to add weight to claims and persuade non-experts to believe statements that may be false or meaningless. They often use established technical terms in idiosyncratic ways, thereby demonstrating unfamiliarity with mainstream work in the discipline.
The easy checklist of pseudoscience vs science. Notice how many anti-vaccine “research” check off the pseudoscience.

Pseudoscience vs science – no contest, science wins

So let’s review the pseudoscience vs science for vaccines (but it can be applied to any scientific “controversy” invented by the denialists).

Real science gathers evidence over and over until the possibility that the original hypothesis is wrong becomes vanishingly small. The evidence is always gathered in an unbiased manner if one takes the average of all the studies.

Yes, there are some scientists that attempt to support their own pre-ordained beliefs. But that’s why real science requires lots of repeated evidence from many different researchers over a long period of time. Bad science eventually gets discarded.

Science is not magical thinking. A scientist doesn’t proclaim that vaccines are safe and effective because they have faith in that claims – they do so because they have evidence produced in a scientific manner. 

Pseudoscience is magical thinking. It only uses biased evidence or cherry-picking one study that happens to somewhat support their beliefs. It does not rely upon solid evidence.

Pseudoscience, which is all the anti-vaccine religion knows, is intellectually lazy. It takes only a few neurons to invent conspiracies, to reject high-powered science, to cherry-pick lame studies, and to use anecdotes as data.

Real science, on the other hand, takes real work. It takes reading published articles beyond a few sentences of the abstract. It takes years of study. But it can be done.

So if you think evolution is wrong, or vaccines are unsafe, or climate change is bogus, then get off your lazy ass, go to college to get a degree in legitimate science, then go to a respected graduate school and get a degree in a related science, then do a post-doctoral position in a world-respected science lab, then publish papers, then go on to a respected faculty research position, then publish more papers, then stand up in major scientific conferences presenting your hypothesis and data. Once you do all of that, maybe we’ll consider your scientific evidence that contradicts the consensus.

This takes about 15 years of your life, but it’s much easier to do your one hour on Google. Then again, after spending 15 years actually studying the science of vaccines, you’ll just become pro-vaccine, pro-evolution, and pro-climate change. 

In the “battle” of pseudoscience vs science, science wins. Because pseudoscience is pure, unfettered, bovine feces. And it still stinks, no matter how much perfume you throw on it.

Editor’s note: This article combines elements of several articles about pseudoscience published in 2012 and 2013. It has been substantially revised.

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The Original Skeptical Raptor
Chief Executive Officer at SkepticalRaptor
Lifetime lover of science, especially biomedical research. Spent years in academics, business development, research, and traveling the world shilling for Big Pharma. I love sports, mostly college basketball and football, hockey, and baseball. I enjoy great food and intelligent conversation. And a delicious morning coffee!