| 翻訳と辞書 |
| Circular analysis ： ウィキペディア英語版 |
Circular analysis
In statistics, circular analysis is the selection of the details of a data analysis using the data that is being analysed. It is often referred to as double dipping, as one uses the same data twice. Circular analysis unjustifiably inflates the apparent statistical strength of any results reported and, at the most extreme, can lead to the apparently significant result being found in data that consists only of noise. In particular, where an experiment is implemented to study a postulated effect, it is a misuse of statistics to initially reduce the complete dataset by selecting a subset of data in ways that are aligned to the effects being studied. A second misuse occurs where the performance of a fitted model or classification rule is reported as a raw result, without allowing for the effects of model-selection and the tuning of parameters based on the data being analyzed.
==Examples==
At its most simple, it can include the decision to remove outliers, after noticing this might help improve the analysis of an experiment. The effect can be more subtle. In functional magnetic resonance imaging (fMRI) data, for example, considerable amounts of pre-processing is often needed. These might be applied incrementally until the analysis 'works'. Similarly, the classifiers used in a multivoxel pattern analysis of fMRI data require parameters, which could be tuned to maximise the classification accuracy.
In geology, the potential for circular analysis has been noted in the case of maps of geological faults, where these may be drawn on the basis of an assumption that faults develop and propagate in a particular way, with those maps being later used as evidence that faults do actually develop in that way.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「Circular analysis」の詳細全文を読む
スポンサード リンク
| 翻訳と辞書 : 翻訳のためのインターネットリソース |
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.
|
|