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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]
For example, if a server makes twenty dollars more than an average night, a positive feeling will be evoked. If a student earns a lower grade than is typical, a negative feeling will be evoked. Generally, upward counterfactuals are likely to result in a negative mood, while downward counterfactuals elicit positive moods.
Falsifiability or defeasibility, which means that counterexamples to the hypothesis are logically possible. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is a possibility of deciding whether it is true or false based on experimentation by anyone.
After experiencing a bad outcome with a decision problem, the tendency to avoid the choice previously made when faced with the same decision problem again, even though the choice was optimal. Also known as "once bitten, twice shy" or "hot stove effect". [105] Mere exposure effect or familiarity principle (in social psychology)
Bernard's recommendation that an experiment's observer should not know the hypothesis being tested contrasted starkly with the prevalent Enlightenment-era attitude that scientific observation can only be objectively valid when undertaken by a well-educated, informed scientist. [8]
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 ...
If the true rule (T) encompasses the current hypothesis (H), then positive tests (examining an H to see if it is T) will not show that the hypothesis is false. If the true rule (T) overlaps the current hypothesis (H), then either a negative test or a positive test can potentially falsify H.
It accounts for the fact that many biases are self-motivated or self-directed (e.g., illusion of asymmetric insight, self-serving bias). There are also biases in how subjects evaluate in-groups or out-groups; evaluating in-groups as more diverse and "better" in many respects, even when those groups are arbitrarily defined ( ingroup bias ...