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This is the problem of induction. Suppose we want to put the hypothesis that all swans are white to the test. We come across a white swan. We cannot validly argue (or induce) from "here is a white swan" to "all swans are white"; doing so would require a logical fallacy such as, for example, affirming the consequent. [3]
The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H 0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have ...
Note that data dredging is a valid way of finding a possible hypothesis but that hypothesis must then be tested with data not used in the original dredging. The misuse comes in when that hypothesis is stated as fact without further validation. "You cannot legitimately test a hypothesis on the same data that first suggested that hypothesis.
Proving too much – an argument that results in an overly generalized conclusion (e.g.: arguing that drinking alcohol is bad because in some instances it has led to spousal or child abuse). Psychologist's fallacy – an observer presupposes the objectivity of their own perspective when analyzing a behavioral event.
In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false negatives that reject the alternative hypothesis when it is true). [a]
The just-world fallacy, or just-world hypothesis, is the cognitive bias that assumes that "people get what they deserve" – that actions will necessarily have morally fair and fitting consequences for the actor. For example, the assumptions that noble actions will eventually be rewarded and evil actions will eventually be punished fall under ...
A good example of this is a study showed that when making food choices for the coming week, 74% of participants chose fruit, whereas when the food choice was for the current day, 70% chose chocolate. Insensitivity to sample size, the tendency to under-expect variation in small samples.
Example 3. In other cases it may simply be unclear which is the cause and which is the effect. For example: Children that watch a lot of TV are the most violent. Clearly, TV makes children more violent. This could easily be the other way round; that is, violent children like watching more TV than less violent ones. Example 4