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HOW TO USE GRAPHS TO DIAGNOSE AND DEAL WITH BAD EXPERIMENTAL DATA

机译:如何使用图表对不良实验数据进行诊断和处理

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摘要

This talk deals with thorny issues that confront every experimenter - how to deal with individual results that do not appear to fit with the rest of the data. It provides graphical tools that will help experimenters properly diagnose what's really wrong with their data - damaging outliers and/or a need for response transformation. The trick is to maintain a reasonable balance between two types of errors: 1. Deleting data that vary only due to common causes, thus introducing bias to the conclusions. 2. Not detecting true outliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to breakthrough improvements (making discoveries more or less by-accident). Attendees will see two real-life data sets that don't reveal their secrets at first glance. However, with the aid of various diagnostic plots (readily available in off-the-shelf statistical software), it becomes much clearer what needs to be done. Armed with this knowledge, quality professionals will be much more likely to draw the proper conclusions from experiments that produce bad (discrepant) data.
机译:本演讲涉及每个实验者面临的棘手问题-如何处理似乎与其余数据不符的单个结果。它提供了图形化工具,可以帮助实验者正确诊断数据的真正错误-损坏异常值和/或需要响应转换。诀窍是要在两种类型的错误之间保持合理的平衡:1.删除仅由于常见原因而变化的数据,从而使结论有偏差。 2.未检测到由于特殊原因而发生的真实异常值。这样的异常值可能掩盖真实的效果或导致错误的结论。此外,可能会失去机会来学习可预防的失败原因或可重现的条件,从而导致突破性的改进(或多或少是偶然发现)。与会者将看到两个真实的数据集,这些数据集乍一看并没有揭示他们的秘密。但是,借助各种诊断图(现成的统计软件中已有),变得更清楚需要做什么。有了这些知识,高质量的专业人员将更有可能从产生错误数据的实验中得出正确的结论。

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