Data Entry Error Rate Calculation
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Bit Error Rate Calculation
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Download Overview Request a Demo When Good Info Goes Bad: The Real Cost of Human Data Errors – Part 1 of 2 Home>Blog>When Good Info Goes Bad: The Real Cost of Human Data Errors – Part 1 of 2 Matt Harris 19 May 2014 At 2:45 pm on May 6, 2010, Wall Street essentially had a heart attack. In just minutes, the stock type 1 error rate calculation market plunged 1000 points, for reasons traders, analysts, and business media could not explain. The “flash crash” wiped out $1.1 Trillion of investor dollars and even though most of that was quickly regained, it left the market badly shaken. What happened? It appears that a single keystroke error was to blame. The letter “B” was inserted in a sell order instead of the letter “M”. Billion was input where Million should have been and it triggered a ripple effect through the automated financial markets. Costly errors in the events business might not have as many zeros as that epic fail, but when it’s your event or your exhibitor who has to deal with a problem caused by a keystroke mistake, it can seem just as bad. Today a surprising amount of venue managers and event organizers still work with separate CRM, operations, and financial systems that either require them to manually enter data multiple times, or have one-way information flow from system to system that can get out of sync. The result is costly – and often embarrassing – errors that stem from bad or out-of-step event detail data
Events Submit an Event News Read News Submit News Jobs Visit the Jobs Board Search Jobs Post a Job Marketplace Visit the Marketplace Assessments Case Studies how to calculate error rate statistics Certification E-books Project Examples Reference Guides Research Templates Training Materials & Aids how to calculate error rate in excel Videos Newsletters Join71,768 other iSixSigma newsletter subscribers: THURSDAY, OCTOBER 06, 2016 Font Size Login Register Topic Sampling plan how to calculate error rate from confusion matrix for QC of data entry errors Sampling plan for QC of data entry errors Home › Forums › Old Forums › General › Sampling plan for QC of data entry https://ungerboeck.com/blog/when-good-info-goes-bad-the-real-cost-of-human-data-errors-part-1-of-2 errors This topic contains 11 replies, has 6 voices, and was last updated by U 10 years, 12 months ago. Viewing 12 posts - 1 through 12 (of 12 total) Author Posts Tweet October 4, 2005 at 2:58 pm #92573 Transactional BBMember @Transactional-BB Reputation - 0 Rank - Aluminum Hello Everyone I am trying to develop a sampling plan for https://www.isixsigma.com/topic/sampling-plan-for-qc-of-data-entry-errors/ QC of a data entry process. For example, suppose that I have assigned a project to an outside vendor and the objective of the project is to input the names, addresses and phone numbers of all the people in the phone book into an excel sheet and deliver it to me. When I receive that file I would not like to 100% QC it or in other words go through the entire phonebook and figure out whether the file i received from the vendor has less than a preivously determined acceptable level of error rate. Does someone know of a method for calculating the sample size for such a QC process? My confidence level is 95%, maximum acceptable error level would vary but we can assume 5% for now and the population is finite. Thanks. October 4, 2005 at 3:28 pm #92575 ABParticipant @AB Reputation - 0 Rank - Aluminum Check these links. The first is an article that will help you understand the calculation. The second is a tool you can use to calculate sample size. http://www.isixsigma.com/library/content/c
Help Latest content Archive eLetters RSS Home > Volume 3, Issue 5 > Article BMJ http://bmjopen.bmj.com/content/3/5/e002406.full Open 2013;3:e002406 doi:10.1136/bmjopen-2012-002406 Health informatics Research Error rates in a clinical data repository: lessons from the transition to electronic data transfer—a descriptive study Matthew K H Hong1, Henry H I Yao1, John S Pedersen2, Justin S Peters1, Anthony J Costello1, Declan G Murphy1,3, Christopher M Hovens1, Niall M error rate Corcoran1 1Division of Urology, Department of Surgery, University of Melbourne, Royal Melbourne Hospital and the Australian Prostate Cancer Research Centre Epworth, Melbourne, Victoria, Australia 2TissuPath Specialist Pathology, Mount Waverley and Monash University Faculty of Medicine, Melbourne, Victoria, Australia 3Division of Cancer Surgery, Peter MacCallum Cancer Centre, East Melbourne, Victoria, error rate calculation Australia Correspondence to Dr Matthew Hong; m.k.hong{at}ausdoctors.net Received 26 November 2012 Accepted 9 April 2013 Published 17 May 2013 Next Section Abstract Objective Data errors are a well-documented part of clinical datasets as is their potential to confound downstream analysis. In this study, we explore the reliability of manually transcribed data across different pathology fields in a prostate cancer database and also measure error rates attributable to the source data. Design Descriptive study. Setting Specialist urology service at a single centre in metropolitan Victoria in Australia. Participants Between 2004 and 2011, 1471 patients underwent radical prostatectomy at our institution. In a large proportion of these cases, clinicopathological variables were recorded by manual data-entry. In 2011, we obtained electronic versions of the same printed pathology reports for our cohort. The data were electronically imported in parallel to any existing manual entry record enabling direct comparison
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