Logic, Prejudice, and Wicca

By: FTP

I had a great conversation with a friend over lunch about some of the current events going on in the world. My friend, who is an active supporter of gay rights, was discussing a response he had received from Member of Parliament, John G. Williams who strongly opposes same-sex marriage. My friend was angry at the unsubstantiated 'facts' and opinions that Mr. Williams was basing his political position upon in denying same-sex couples equal access to marriage.

This got me to thinking about logical fallacies and evaluating sources of information to determine their validity. If ever there were two minority communities that have suffered the slings and arrows of spurious reasoning, it would be the gay and Pagan communities.

There are three basic types of information gathering: empirical, correlational, and opinion. Empirical information is gained by taking at least two identical samples of a population, varying a single variable while holding all other variables constant, and then observing if any difference occurs between the groups. This method allows for the greatest confidence that any difference that occurs between the groups is due to the single variable that differed between them. A medical example would be a group of people randomly divided into two groups: one group injected with a saline solution and the other injected with an experimental drug. With both groups unaware of which injection they had received, it would be reasonable to conclude that differences in recovery rates or side effects were due to the type of injection received. Unfortunately, empirical evidence is not always possible or ethical to collect.

Correlational data comes from observing at least two variables and seeing if a relationship exists between them. To keep with the example above, a researcher might review medical records of patients who had been receiving a new drug therapy versus the records of patients who had not received the therapy. The problem however, is that you can not infer causation from correlation. Differences between the two groups could be due to any number of unaccounted for factors such as differences in socio-economic status, diet, other medical therapies used, severity of symptoms, etc. Inferring causation would be akin to suggesting that ice cream consumption causes heat stroke since ice cream sales have a positive correlation to the occurrence rate of heat stroke. In this example, it is obvious that a third variable, high climate temperature, likely causes both the rate of rate ice cream sales and heat strokes to increase.

Opinion is the least useful of these three sources of information as it is usually based on unverified assumptions or ambiguous reasoning: “I believe something is true, because I believe it to be so." This isn't to say that opinion can't be a good starting point of a discussion or theory, however, since opinion lacks a verifiable source it can't be taken as fact. Discriminating which type of information source you are being presented with in a conversation, during a newscast, or while reading an article can be difficult, but it is good to keep this in mind while determining how much weight you will give it.

One other common issue I'd like to touch on is evaluating statistics. On a daily basis we run across statistics being used to justify social, economic, and political actions. But what do they really mean and are the results truly so black and white?

When examining two groups of people, researchers usually gather data along a range of values that often form a 'bell curve' (named so due to its bell-like shape). Let me attempt some ASCII art and see if it comes across well:

  y

  |             | 

  |           _-|-_

  |         _-  |  -_

  |      __-    |     -__

  | ___--       |        --___

  +-------------|------------|----x

  0             a           100

In the above graph, the x-axis would represent the variable being studied (for example, aggressiveness) which I have given a range of 0 (no aggression) to 100 (very high aggression) as determined by a defined measurement (ie. questionnaire or behavioural testing). The y-axis represents how commonly any given score was achieved, usually finding a clustering of scores in the middle and less scores at the extremes. Point 'a' on the x-axis is the mean (or average) value of the scores.

When researchers compare two groups to each other on this basis, data is usually presented by overlaying the two bell curves and comparing their mean scores. One commonly misunderstood field of such statistics in the media are differences between the sexes. For discussion's sake, let's assume that the above study found that the mean aggression score was 55 for men and 50 for women. The common logical error I see many people make is to say that men are therefore more aggressive, but, to say that would be an erroneous generalization.

Let's look back at the bell curve and understand it given this example. Both population samples (men and women in this case) are comprised of scores ranging from 0 to 100. Both populations contain members that are near the average score and near the extreme scores. There will be men who are passive, the majority near the average, and some with high aggression, the same for the distribution of scores for women. While the mean score of the two groups may vary, there will be men who are more aggressive than some women, and women who are more aggressive than some men. Making the logical leap that the difference between the two average scores infers that men are more aggressive than women would be a false generalization. In this case it would be more accurate to say that there is a tendency for men to be more aggressive than women although there is a high degree of overlap in aggression levels.

When I started writing this, I had hoped to make a fairly short and concise article on consuming information intelligently which I don't think I've managed to do. The topic however is one that I have a lot of passion for as there are many newscasters, politicians, and everyday people that often base their actions and beliefs upon information that they have not evaluated properly. Such poor decision making can have serious real-life consequences in how people or groups of people are treated. Logical errors and generalizations can lead to stereotyping and prejudice. I'm sure we've all been at fault of this ourselves at one point or another and have also been on the unfortunate receiving end,

As a community of people that strive along a path of wisdom, and walk in both the rational and non-rational worlds, we do ourselves service by being mindful of how we consume, interpret, and apply information whether magickal or mundane. Much religious belief is based on faith, which many would argue is simply unfounded opinion. It is a tough argument to respond to, as the response comes from the non-rational (and I don't mean 'irrational') world which can not be expressed well by logic and rational words. When I sense the divine in the living world around me, when I experience the heart-clenching beauty and love of the God and Goddess in a circle, when I experience an act of magick...none of these things will ever be conveyed by words beyond a thin shadow of their true and powerful subjective existence. Such is the nature of Wicca and other spiritual paths.