We frequently use the term “pseudoscience” to describe the ideology of certain groups: antivaccinationists, evolution deniers (creationists), global warming deniers, HIV/AIDS denialism, and almost anything in the areas of parapsychology, alternative medicine, and sasquatch. The science denialists (broadly defined as any group who rejects the scientific consensus on any subject without valid scientific support) always seem to be insulted by the word “pseudoscience”, even though the name is given to them both as a pejorative, but also because its based on their non-scientific, but scientific-sounding method of providing information.
In fact, there are several hallmarks that indicate to most educated individuals as to what is or is not pseudoscience. Real science is a systematic and rational method to organize and analyze “knowledge” into testable explanations and predictions. Sometimes, it appears that the anti-science crowd believes that science is just a word, not a philosophy which is organized as the scientific method. It isn’t some magical system that only smart people in secret ivory towers practice. The scientific method is simply a set of logical steps:
- Formulate a question: Based on observations of the natural world. Maybe you notice that sky is blue, and you ask “why is the sky blue?” Or “how do I design a vaccine to encourage the immune system to prevent a virus from causing a disease?” Of course, the questions can become much more complex as we make more detailed observations of the our world.
- Hypothesis: An hypothesis is a conjecture, based on the knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis may be broad or very narrow. One could make a hypothesis that life can evolve on many planets across the universe. Or one could make a hypothesis that a drug can cure a disease in a small population of individuals. A proper hypothesis must include a null hypothesis, that is, the scientist must be willing to test that the null hypothesis is also false (a sort of double negative). This null hypothesis is that the new vaccine does nothing and that any disease prevention are due to chance effects. Researchers must also show that the null hypothesis is false. A scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested. This all sounds complicated, but digested down to its simplest form, it means that a scientist is always willing to attempt to prove that the hypothesis is wrong.
- Prediction: Once a hypothesis is developed, then the a prediction (or more than one prediction) is made based on the hypothesis. For example, if a vaccine is supposed to prevent a disease, then the prediction is made that it prevents some some amount of the disease above what would be assumed just by random chance. For example, without the vaccine it might be predicted that only 10% of individuals might be immune to the disease, but with the vaccine, it would be predicted that 85% would be immune. In all fields of science, the hypothesis leads to predictions which are different than what would be found simply by coincidence or randomness. Also, the hypothesis must be powerful enough to create more accurate predictions than alternative hypotheses.
- Test: This is the conducting of experiments or investigations to determine whether the real world behaves as predicted by the hypotheses. These experiments are observations which will agree with or conflict with the predictions; if they agree, then the confidence in the hypothesis will increase. On the other hand, if there is conflict, the confidence will, of course, decrease. Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. Medical and drug experiments utilize double-blind clinical trials to limit confirmation bias, a tendency towards confirmation of the hypothesis under study.
- Analysis: This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and one’s own experience can be incorporated at any stage in the process. Many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.Read More »Pseudoscience and vaccine denialism (updated)
