Truth and Statistics – Food for Thought

Appeal to Ignorance: A logical fallacy: taking the absence of evidence to be evidence of absence. If something is not

known to be false, assume that it is true; or if something is not known to be true, assume that it is false. For example, if I

have no reason to think that anyone in Tajikistan wishes me well, that is not evidence that nobody in Tajikistan wishes

me well.

Average: An ambiguous term. It often denotes the arithmetic mean but it can also denote the median, the geometric

mean, a weighted mean – which is derived from the inclusion of external factors and weighting of those factors to derive

a result, among other things. Beware if some report you read quotes an average without making it clear which average

is being quoted.

Bias: A measurement is said to be biased if, on the average, it gives an answer that differs from the truth. For example,

if you get on the scale with clothes on, that biases the actual weight measurement to be higher than your true weight.

Convenience Sample: A sample drawn because of its convenience; it is not a probability sample. For example, I

might take a sample of opinions by just asking my 10 nearest neighbors. That would be a sample of convenience, and

would be unlikely to be representative of a true targeted audience of significant size that could give me results that

would actually be usable as part of a serious survey.

Ask: Why Am I Being Shown This and What Does This Statistic Really Tell Me?

Be Careful How You Use Statistics as You Can Easily and Unintentionally Mislead People

Conclusion and Advice

Understand how people most likely would react when they view the statistic

Have integrity as you present statistics to others while marketing to them

Add verbiage to explain what the statistic means - the essence of what it is saying when there is not enough clarity within the statistic itself

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