Comparison Error Definition
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when considering a result under error definition chemistry many hypotheses, some tests will give false error definition physics positives; many statisticians make use of Bonferroni correction, false discovery rate, and error computer definition other methods to determine the odds of a negative result appearing to be positive. References[edit] ^ Benjamini, Yoav; Hochberg,
Percent Error Definition
Yosef (1995). "Controlling the false discovery rate: a practical and powerful approach to multiple testing" (PDF). Journal of the Royal Statistical Society, Series B. 57 (1): 289–300. MR1325392. Retrieved from "https://en.wikipedia.org/w/index.php?title=Per-comparison_error_rate&oldid=672691707" Categories: Hypothesis testingRates Navigation menu Personal tools Not experimental error definition logged inTalkContributionsCreate accountLog in Namespaces Article Talk Variants Views Read Edit View history More Search Navigation Main pageContentsFeatured contentCurrent eventsRandom articleDonate to WikipediaWikipedia store Interaction HelpAbout WikipediaCommunity portalRecent changesContact page Tools What links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationWikidata itemCite this page Print/export Create a bookDownload as PDFPrintable version Languages Add links This page was last modified on 23 July 2015, at 06:40. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view
may be challenged and removed. (June 2016) (Learn how and when to remove this template message) An example of data produced by data dredging, apparently showing a close link between relative error definition the letters in the winning word used in a spelling bee competition
Systematic Error Definition
and the number of people in the United States killed by venomous spiders. The clear similarity in trends
Fundamental Attribution Error Definition
is a coincidence. If many data series are compared, similarly convincing but coincidental data may be obtained. In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers https://en.wikipedia.org/wiki/Per-comparison_error_rate a set of statistical inferences simultaneously[1] or infers a subset of parameters selected based on the observed values.[2] It is also known as the look-elsewhere effect. Errors in inference, including confidence intervals that fail to include their corresponding population parameters or hypothesis tests that incorrectly reject the null hypothesis, are more likely to occur when one considers the set as a https://en.wikipedia.org/wiki/Multiple_comparisons_problem whole. Several statistical techniques have been developed to prevent this from happening, allowing significance levels for single and multiple comparisons to be directly compared. These techniques generally require a higher significance threshold for individual comparisons, so as to compensate for the number of inferences being made. Contents 1 History 2 Definition 2.1 Classification of multiple hypothesis tests 3 Example 4 Controlling procedures 5 Post-hoc testing of ANOVAs 6 Large-scale multiple testing 6.1 Assessing whether any alternative hypotheses are true 7 See also 8 References 9 Further reading History[edit] The interest in the problem of multiple comparisons began in the 1950s with the work of Tukey and Scheffé. New methods and procedures came out: the closed testing procedure (Marcus et al., 1976) and the Holm–Bonferroni method (1979). Later, in the 1980s, the issue of multiple comparisons came back (Hochberg and Tamhane (1987), Westfall and Young (1993), and Hsu (1996)). In 1995 work on the false discovery rate and other new ideas began. In 1996 the first conference on multiple comparisons took place in Israel. This was followed by conferences around the w
the same result through different correct methods) often differ slightly, and a simple equality test fails. For example: float a = 0.15 + 0.15 float b = 0.1 + 0.2 if(a == b) // can be false! if(a >= b) // can also be false! Don’t http://floating-point-gui.de/errors/comparison/ use absolute error margins The solution is to check not whether the numbers are exactly the same, but whether their difference is very small. The error margin that the difference is compared to is often called epsilon. The most simple form: http://www.businessdictionary.com/definition/contrast-error.html if( Math.abs(a-b) < 0.00001) // wrong - don't do this This is a bad way to do it because a fixed epsilon chosen because it “looks small” could actually be way too large when the numbers being compared are very small as error definition well. The comparison would return “true” for numbers that are quite different. And when the numbers are very large, the epsilon could end up being smaller than the smallest rounding error, so that the comparison always returns “false”. Therefore, it is necessary to see whether the relative error is smaller than epsilon: if( Math.abs((a-b)/b) < 0.00001 ) // still not right! Look out for edge cases There are some important special cases where this will fail: When both a and b are zero. comparison error definition 0.0/0.0 is “not a number”, which causes an exception on some platforms, or returns false for all comparisons. When only b is zero, the division yields “infinity”, which may also cause an exception, or is greater than epsilon even when a is smaller. It returns false when both a and b are very small but on opposite sides of zero, even when they’re the smallest possible non-zero numbers. Also, the result is not commutative (nearlyEquals(a,b) is not always the same as nearlyEquals(b,a)). To fix these problems, the code has to get a lot more complex, so we really need to put it into a function of its own: public static boolean nearlyEqual(float a, float b, float epsilon) { final float absA = Math.abs(a); final float absB = Math.abs(b); final float diff = Math.abs(a - b); if (a == b) { // shortcut, handles infinities return true; } else if (a == 0 || b == 0 || diff < Float.MIN_NORMAL) { // a or b is zero or both are extremely close to it // relative error is less meaningful here return diff < (epsilon * Float.MIN_NORMAL); } else { // use relative error return diff / Math.min((absA + absB), Float.MAX_VALUE) < epsilon; } } This method passes tests for many important special cases, but as you can see, it uses some quite non-obvious logic. In particular, it has to use a completely different definition of error margin when a or b is zero, because the classica
Sign Up Subjects TOD contrast error Definition + Create New Flashcard Popular Terms In interview or performance appraisal process, error caused by the effect of previously interviewed or appraised applicants on the interviewer. It results in a conscious or subconscious comparison of one applicant with another, and tends to exaggerate the differences between the two. dislocated work... organizational... technical skill job specificati... motivation values job design recruitment human resource... You Also Might Like... Ravinder Kapur Should I Rank My Employees? Every business organization struggles to get the best out of its employees. To achieve this, they have to ensure that they retain their top performers and get their remaining workers to improve their productivity and effectiveness. One way that ... Read more Jeffrey Glen Advise vs. Advice ADVERTISEMENT Adam Colgate Want to Increase Your Credit Score Quickly? Here ... Ravinder Kapur What are the Common Mistakes of New Managers? Email Print Embed Copy & paste this HTML in your website to link to this page contrast error Browse Dictionary by Letter: # A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Never miss another term. Sign up for our FREE newsletter today! © 2016 WebFinance Inc. All Rights Reserved.Unauthorized duplication, in whole or in part, is strictly prohibited. Privacy, Disclaimers & Copyright COMPANY About Us Contact Us Advertise with Us Careers RESOURCES Articles Flashcards Citations All Topics FOLLOW US OUR APPS