Probability Of Acceptance Error
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removed. (December 2009) (Learn how and when to remove this template message) In statistics, the term "error" arises in two ways. Firstly, it arises in the context
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of decision making, where the probability of error may be considered as probability error definition being the probability of making a wrong decision and which would have a different value for each type of probability of error formula error. Secondly, it arises in the context of statistical modelling (for example regression) where the model's predicted value may be in error regarding the observed outcome and where the term
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probability of error may refer to the probabilities of various amounts of error occurring. Hypothesis testing[edit] In hypothesis testing in statistics, two types of error are distinguished. Type I errors which consist of rejecting a null hypothesis that is true; this amounts to a false positive result. Type II errors which consist of failing to reject a null hypothesis that is false;
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this amounts to a false negative result. The probability of error is similarly distinguished. For a Type I error, it is shown as α (alpha) and is known as the size of the test and is 1 minus the specificity of the test. It should also be noted that α (alpha) is sometimes referred to as the confidence of the test, or the level of significance (LOS) of the test. For a Type II error, it is shown as β (beta) and is 1 minus the power or 1 minus the sensitivity of the test. Statistical and econometric modelling[edit] The fitting of many models in statistics and econometrics usually seeks to minimise the difference between observed and predicted or theoretical values. This difference is known as an error, though when observed it would be better described as a residual. The error is taken to be a random variable and as such has a probability distribution. Thus distribution can be used to calculate the probabilities of errors with values within any given range. This statistics-related article is a stub. You can help Wikipedia by expand
false positives and false negatives. In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is incorrectly retaining a false null hypothesis (a "false negative").[1] More simply stated, a type probability of error and bit error rate I error is detecting an effect that is not present, while a type II error is
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failing to detect an effect that is present. Contents 1 Definition 2 Statistical test theory 2.1 Type I error 2.2 Type II error 2.3 probability of error statistics Table of error types 3 Examples 3.1 Example 1 3.2 Example 2 3.3 Example 3 3.4 Example 4 4 Etymology 5 Related terms 5.1 Null hypothesis 5.2 Statistical significance 6 Application domains 6.1 Inventory control 6.2 Computers 6.2.1 Computer security https://en.wikipedia.org/wiki/Probability_of_error 6.2.2 Spam filtering 6.2.3 Malware 6.2.4 Optical character recognition 6.3 Security screening 6.4 Biometrics 6.5 Medicine 6.5.1 Medical screening 6.5.2 Medical testing 6.6 Paranormal investigation 7 See also 8 Notes 9 References 10 External links Definition[edit] In statistics, a null hypothesis is a statement that one seeks to nullify with evidence to the contrary. Most commonly it is a statement that the phenomenon being studied produces no effect or makes no difference. An example of a null hypothesis is the statement https://en.wikipedia.org/wiki/Type_I_and_type_II_errors "This diet has no effect on people's weight." Usually, an experimenter frames a null hypothesis with the intent of rejecting it: that is, intending to run an experiment which produces data that shows that the phenomenon under study does make a difference.[2] In some cases there is a specific alternative hypothesis that is opposed to the null hypothesis, in other cases the alternative hypothesis is not explicitly stated, or is simply "the null hypothesis is false" – in either event, this is a binary judgment, but the interpretation differs and is a matter of significant dispute in statistics. A typeI error (or error of the first kind) is the incorrect rejection of a true null hypothesis. Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't. Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not. A typeII error (or error of the second kind) is the failure to reject a false null hypothesis. Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who real
on how the hypothesis is being tested. P is also described in terms of rejecting H0 when it is actually true, however, it is not a direct probability of this state. The null http://www.statsdirect.com/help/basics/p_values.htm hypothesis is usually an hypothesis of "no difference" e.g. no difference between blood pressures in group A and group B. Define a null hypothesis for each study question clearly before the start of your study. The http://searchsecurity.techtarget.com/definition/false-acceptance only situation in which you should use a one sided P value is when a large change in an unexpected direction would have absolutely no relevance to your study. This situation is unusual; if you are probability of in any doubt then use a two sided P value. The term significance level (alpha) is used to refer to a pre-chosen probability and the term "P value" is used to indicate a probability that you calculate after a given study. The alternative hypothesis (H1) is the opposite of the null hypothesis; in plain language terms this is usually the hypothesis you set out to investigate. For example, question is "is there probability of error a significant (not due to chance) difference in blood pressures between groups A and B if we give group A the test drug and group B a sugar pill?" and alternative hypothesis is " there is a difference in blood pressures between groups A and B if we give group A the test drug and group B a sugar pill". If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis. It does NOT imply a "meaningful" or "important" difference; that is for you to decide when considering the real-world relevance of your result. The choice of significance level at which you reject H0 is arbitrary. Conventionally the 5% (less than 1 in 20 chance of being wrong), 1% and 0.1% (P < 0.05, 0.01 and 0.001) levels have been used. These numbers can give a false sense of security. In the ideal world, we would be able to define a "perfectly" random sample, the most appropriate test and one definitive conclusion. We simply cannot. What we can do is try to optimise all stages of our research to minimise sources of uncertainty. When presenting P values some groups find it helpful to use the asterisk rating syst
Biometric Technology User Authentication Services View All Enterprise Single Sign-On (SSO) PKI and Digital Certificates Security Token and Smart Card Technology Two-Factor and Multifactor Authentication Strategies Identity management View All Active Directory and LDAP Security Enterprise User Provisioning Tools Password Management and Policy User Authentication Services View All Biometric Technology Enterprise Single Sign-On (SSO) PKI and Digital Certificates Security Token and Smart Card Technology Two-Factor and Multifactor Authentication Strategies Web Authentication and Access Control View All Please select a category Identity management User Authentication Services Web Authentication and Access Control Section Get Started News Get Started Evaluate Manage Problem Solve Sponsored Communities Home Biometric Technology Network security false acceptance (type II error) Definition false acceptance (type II error) Posted by: Margaret Rouse WhatIs.com Share this item with your network: Sponsored News ABC’s of VDI in 2016 –Dell Predictable VDI Performance From PoC to Production –SimpliVity See More Vendor Resources Social Engineering in IT Security –ComputerWeekly.com Presentation Transcript: Changing Authentication Options –ActivIdentity Corporation False acceptance, also called a type II error, is a mistake occasionally made by biometric security systems. In an instance of false acceptance, an unauthorized person is identified as an authorized person. Download this free guide Download Your Guide to the ISACA CISM Certification Take a closer look at the ISACA Certified Information Security Manager certification, including the value it provides security professionals, how it compares to other security professionals, and what the CSX program offers Start Download Corporate E-mail Address: You forgot to provide an Email Address. This email address doesn’t appear to be valid. This email address is already registered. Please login. You have exceeded the maximum character limit. Please provide a Corporate E-mail Address. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers. You also agree that your personal information may be transferred and p