Basic Logical Fallacies and Experimental Social Science

In Social Science, the holy grail is in the logic that is used to formulate hypothesis and in the design of quasi experiments, where complete control over subjects, variables and even intervening variables is not possible. The arguments in favor of and the argument against the hypothesis and the null hypothesis present minefields of logical fallacy.  In addition, statistics are heavily used, but the average person can be subjected to interpretations of the data that contain fallacies.

Two important fallacies lie in the connection between claim and evidence. In many cases of logical fallacy in experimental design, there is a weak conceptual, factual or logical link between the claim and the evidence.

 One fallacy involves correlation versus causation. The easiest example is when two events occur at the same time, or in close apparent relation with each other. One is then seen as the cause of the other. An increase in robberies occurs at the same time that jobs cuts are announced, for example. There is no evidence to support the claim that the expected job losses caused the increase in robberies.

Overgeneralization or oversimplification is another framework for logical fallacies. Generalizing from too small of a population, or reducing complex social issues to only one choice or the other, and leaving out the complex issues that do not support the claim are examples of fallacies that stem from overgeneralization and oversimplification. Predicting that an increase in crime will result from moving people into new housing development, then stating that people from the housing development committed the crimes is an example. Failing to recognize that most large economies have both capatalistic and socialistic elements is a flaw that comes from oversimplifying complex economies.

Citing lack of evidence to support that a claim is true or false is another logical fallacy. There is no proof that A causes B, therefore only something else could possibly have caused B to happen. This is commonly referred to as “appeal to ignorance”, and is a legitimate part of our justice system: absence of proof of committing a crime will set a person free, whether guilty or not.   “Erring on the side of caution” is another logical fallacy that stems from lack of evidence to support a more accurate prediction.

Confirmation bias is the process of choosing only evidence that supports the hypothesis or prediction. When the results of one experiment are disregarded because the results did not confirm the hypothesis, confirmation bias has been done. This may be legitimate when looking for an exact or particular outcome and unsuccessful tests are not giving the desired result, such as finding a formula for plastic with a high melting point or a program that increases reading test scores.

Failing to consider other evidence is dangerous in the social science experiment, especially given the complexities of human interactions and enterprises. False Dichotomies look for the extremes of a spectrum, while ignoring the continuum in between. In the social sciences, it is not always feasable to look for a “yes” or “no” answer when a continuum exists, but arbritrariness in establishing ranges within a continuum can result in multiple false dichotomies which create problems. A person who is one dollar over the income limit for Food Stamps is denied support due to a false dichotomy of one dollar not being very much to support a major decision. However, one hundreth of a decimal point on a field alcohol test might mean the difference between a serious crime and walking free. The same goes for colleges where grade points are calculated to two decimals.

False dichotomies might result from attempting to go to far in attempting to achieve a “scientific” approach. Most social phenomina cannot be extracted and studied with complete isolation of single variables or even with good randomization in selection of test and control subjects.

In social science experimental design, the hard work begins with the careful scrutiny of all aspects of the design, in screening for logical fallacies, and where they are must be allowed , in properly accounting for them.


Massey University