Data Error Find Interesting Just Need Not Process Real Type
Email Big Data Cloud Technology Service Excellence Learning ViewPoints Data Protection choose at least one Which most closely matches your title? - select - CxO Director Individual Manager Owner VP Your relationship to EMC: - select - Employee Customer Partner No Affiliation Some fields are missing or incorrect Big Data Cloud Technology Service Excellence Learning Application Transformation Data Protection Industry Insight IT Transformation Special Content About Authors Contact Big Data Understanding Type I and Type II Errors By Bill Schmarzo Chief Technology Officer, "Dean of Big Data" September 16, 2013 Shares Shares I recently got an inquiry that asked me to clarify the difference between type I and type II errors when doing statistical testing. Let me use this blog to clarify the difference as well as discuss the potential cost ramifications of type I and type II errors. I have also provided some examples at the end of the blog[1]. In statistical test theory, the notion of statistical error is an integral part of hypothesis testing. The statistical test requires an unambiguous statement of a null hypothesis (H0), for example, "this person is healthy", "this accused person is not guilty" or "this product is not broken". The result of the test of the null hypothesis may be positive (healthy, not guilty, not broken) or may be negative (not healthy, guilty, broken). If the result of the test corresponds with reality, then a correct decision has been made (e.g., person is healthy and is tested as healthy, or the person is not healthy and is tested as not healthy). However, if the result of the test does not correspond with reality, then two types of error are distinguished: type I error and type II error. Type I Error (False Positive Error) A type I error occurs when the null hypothesis is true, but is rejected. Let me say this again, a type I error occurs when the null hypothesis is actually true, but was rejected as false by the testing. A type I error, or false positive, is asserting something as true when it is actually false. This false positive error is basically a "false alarm" – a result that indicates a given condition has been fulfilled when it actually has not been fulfilled (i.e., erroneously a positive result has been assumed).
ProductCompareKafka StreamsControl CenterKafka ConnectSubscriptionServicesTrainingProfessional ServicesResourcesOverviewDocumentationSupport LoginCommunity GroupAboutCompanyPartnersNews & EventsCareersContactBlogDownload Stream ProcessingPutting Apache Kafka To Use: A Practical Guide to Building a Stream Data Platform (Part 2) Jay Kreps. February 25, 2015.This is the second part of our guide on streaming data and Apache Kafka. In part one I talked about the uses for real-time data streams and explained our idea of a stream data platform. The remainder of this guide will contain specific advice on how to go about building a stream data platform in your organization.This advice is drawn from our experience building and implementing Kafka at LinkedIn and rolling it out https://infocus.emc.com/william_schmarzo/understanding-type-i-and-type-ii-errors/ across all the data types and systems there. It also comes from four years working with tech companies in Silicon Valley to build Kafka-based stream data platforms in their organizations.This is meant to be a living document. As we learn new techniques, or new tools become available, I'll update it.Getting StartedMuch of the advice in this guide http://www.confluent.io/blog/stream-data-platform-2/ covers techniques that will scale to hundreds or thousands of well formed data streams. No one starts with that, of course. Usually you start with one or two trial applications, often ones that have scalability requirements that make other systems less suitable. Even in this kind of limited deployment, though, the techniques described in this guide will help you to start off with good practices, which is critical as your usage expands.Starting with something more limited is good, it let's you get a hands on feel for what works and what doesn't, so that, when broader adoption comes, you are well prepared for it.RecommendationsI'll give a set of general recommendations for streaming data and Kafka and then discuss some specifics of different types of data.Limit The Number of ClustersIn early experimentation phases it is normal to end up with a few different Kafka clusters as adoption occurs organically in different parts of the organization. However part of the promise of this approach to data management is having a cent
bold red error message to your http://shiny.rstudio.com/articles/validation.html user. This message is often unhelpful because it mentions things that you may understand as a developer, but that your user may not. This article will show you how to craft “validation errors,” errors designed to lead your user through the UI of your Shiny data error app. Validation errors are user-friendly and, unlike the bold red error message, pleasing to the eye. Best of all, validation errors respond directly to your user’s input. We’ll start by creating an app that quickly returns an error message. The server.R and ui.R scripts below data error find make a simple app that displays a table and draws a plot. To make this app, copy these scripts into your working directory and run: library(shiny) runApp() Note: these files need to be the only ones named server.R and ui.R in your working directory. ## server.R shinyServer(function(input, output) { data <- reactive({ get(input$data, 'package:datasets') }) output$plot <- renderPlot({ hist(data()[, 1], col = 'forestgreen', border = 'white') }) output$table <- renderTable({ head(data()) }) }) ## ui.R shinyUI(fluidPage( titlePanel("Validation App"), sidebarLayout( sidebarPanel( selectInput("data", label