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The p-value is not the probability that the null hypothesis is true, or the probability that the alternative hypothesis is false. [2] A p -value can indicate the degree of compatibility between a dataset and a particular hypothetical explanation (such as a null hypothesis).
In another example of near-total neglect of probability, Rottenstreich and Hsee (2001) found that the typical subject was willing to pay $10 to avoid a 99% chance of a painful electric shock, and $7 to avoid a 1% chance of the same shock. They suggest that probability is more likely to be neglected when the outcomes are emotion-arousing.
To promote a neutral (useless) product, a company must find or conduct, for example, 40 studies with a confidence level of 95%. If the product is useless, this would produce one study showing the product was beneficial, one study showing it was harmful, and thirty-eight inconclusive studies (38 is 95% of 40).
For example, when testing the null hypothesis that a distribution is normal with a mean less than or equal to zero against the alternative that the mean is greater than zero (:, variance known), the null hypothesis does not specify the exact probability distribution of the appropriate test statistic.
The p-value is not the probability that the null hypothesis is true, or the probability that the alternative hypothesis is false; it is the probability of obtaining results at least as extreme as the results actually observed under the assumption that the null hypothesis was correct, which can indicate the incompatibility of results with the ...
Neglect of probability, the tendency to completely disregard probability when making a decision under uncertainty. [52] Scope neglect or scope insensitivity, the tendency to be insensitive to the size of a problem when evaluating it. For example, being willing to pay as much to save 2,000 children or 20,000 children.
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
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