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There are many forms of PHI, with the most common being physical storage in the form of paper-based personal health records (PHR). Other types of PHI include electronic health records, wearable technology, and mobile applications. In recent years, there has been a growing number of concerns regarding the safety and privacy of PHI.
The government is exempted from privacy rules regarding national security. HIPAA additionally allows the authorization of protected health information (PHI) in order to aid in threats to public health and safety as long as it follows the good faith requirement - the idea that disclosing of information is necessary to the benefit of the public. [45]
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
As far as the encyclopedia is concerned, a fact is a statement agreed to by the consensus of scholars or experts working on a topic. (New evidence might emerge so that the statement is no longer accepted as a fact; at that time the encyclopedia should be revised.) Assert facts, including facts about opinions—but don't assert opinions themselves.
Although two people can search for the same thing at the same time, they are very likely to get different results based on what that platform deems relevant to their interests, fact or false. [86] Various social media platforms have recently been criticized for encouraging the spread of false information, such as hoaxes, false news, and ...
Leek summarized the key points of agreement as: when talking about the science-wise false discovery rate one has to bring data; there are different frameworks for estimating the science-wise false discovery rate; and "it is pretty unlikely that most published research is false", but that probably varies by one's definition of "most" and "false".
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.
Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...