The Pillars of Scientific Thinking
We have more access to expert information and knowledge, than ever before at any time in history.
Online learning platforms, such as Coursera, allow anyone with an Internet connection to tap into the classrooms of the most prestigious universities in the world and to learn from the experts and professors at the forefront of their fields.
Availability of resources not being a limitation, one could be surprised that public discourse doesn't sound any more educated.
This being the case, let me lay out some of the clear points that make up scientific thinking in any area of knowledge.
Measurable Evidence
For natural sciences, the experiment is the truth. If the theory doesn't agree with the observations, either the measurements or the theory (or both) are wrong. Nature and reality are always right.
If it can't be measured, the most educated guess is that it doesn't exist. The reason for this is...
Falsifiability
The most fundamental aspect, in my opinion, that separates science from pseudoscience -- and other forms of guesswork and superstition -- is the ability of specifying conditions so well as to give the claim a fair chance of being proven incorrect.
If the claim is: medicine M aids with recovery from disease D. One follows administering the medicine M and say the patient still doesn't recover. In this scenario, an example of unfalsifiable claim is: it would have been worse without medicine M.
Realize how for charlatanism and demagoguery any outcome is supportive of their claim. If it works, medicine M works. If doesn't work, medicine M made it better.
Science is not like that. Scientific claims can be proven false, by design.
Experimentation
The golden standard for scientific experiment is the double-blind clinical trial: you select a sample size representative of the population, say 10 thousand people, you administer the medicine M to half of those -- the treatment group -- and placebo to the other half -- the control group.
Note that for the control group everything must be the same as in the treatment group to eliminate even the chance that any uncontrolled for aspect was the contributing factor for the clinical outcome.
A “blind” experiment refers to the fact that the subjects are unaware of whether they are in the treatment or in the control group as to eliminate the hypothesis that their awareness had influence on the clinical outcome.
A double-blind experiment, in addition to the above, refers to the fact that the experimenters -- or the technicians applying the method -- are unaware of whether they are dealing with a treatment or a control patient. Bookkeeping is made behind the scenes and in isolation to the experiment, and only consulted after the trial is complete. This has the added bonus of eliminating the hypothesis that the experimenter's awareness of which group is treatment or control influenced the outcome.
Independent Verifiability
Science recognizes the fallibility of an individual. To eliminate the hypothesis that the results were fabricated or misrepresented, there's great incentives to disprove previous results.
By putting results and claims to the test of independent parties with non-aligned incentives, one can be reasonably sure that the body of work produced along decades of well-researched phenomena is representative of the best humanly conceivable understanding of that subject.
If there's any better model to achieve a better understanding of the world, the success of the results will speak for themselves, and it will be adopted to the degree it expands human understanding and prediction ability.