How can you distinguish good information from misinformation? Misinformation often includes one or more of the following elements:
- Invalid assumptions. An invalid assumption is something you treat as if it were known to be true or false, when in fact it is not. For example, some parents regard hepatitis B immunization as unnecessary, assuming that this is a disease for which their children are not at risk. This is an invalid assumption (read here to know why).
- Logical Fallacies. A logical fallacy is a flaw in an argument that makes the argument illogic or invalid. Some common logical fallacies are ad hominem arguments (attacking those presenting the argument rather than the argument itself); appeals to pity (trying to win support for one’s arguments by appealing to feelings of sympathy or guilt); and arguments from ignorance (claiming that a statement is true only because it has not been proven false, or that it is false only because it has not been proven true) among others.
- Ad hoc hypotheses. An ad hoc (literally, "for this") hypothesis is an adjustment made to a theory just for the purpose of salvaging it from being refuted. Ad hoc explanations try to explain findings that do not fit the original theory.
- False experts or experts who lack the needed expertise. An expert in one field may be completely ignorant in another field. For instance, an expert endocrinologist may be an expert on diabetes but is not likely to be expert about vaccine safety or immunology. Unfortunately, some who may be experts in one field eagerly make claims about things outside their field of expertise.
- Pseudoscience. Pseudoscientific claims cannot be verified by other researchers because they are often ambiguous and not measurable. In most cases, these claims are not submitted to peer review (that is, review by experts) before making them public and the methods are usually difficult to understand, making the observations difficult to replicate. Often, data may be represented to show one outcome when another is the case. Other times the methods that are used are likely to give a predetermined outcome. Only data purporting to support the claims is presented while conflicting data are ignored or discarded.