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| − | [[Exploratory data analysis]] should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a [[type 1 error]]. It is important to always adjust the significance level when testing multiple models with, for example, a [[Bonferroni correction]]. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same [[type 1 error]] that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.{{sfn|Adèr|2008b|pp=361-362}}
| + | 对探索性数据分析的解释应该非常谨慎。当同时测试多个模型时，发现其中至少一个模型具有统计学意义显著的几率很高，但这可能是由于第'''Ⅰ''' 类错误。在测试多个模型时，总是调整显著性水平很重要，例如，使用Bonferroni校正。'''<font color = '#32cd32'>另外，不应该在同一数据集中进行探索性分析后进行验证性分析。one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. </font>''' |
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| − | Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.
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| − | 对探索性数据分析的解释应该非常谨慎。当同时测试多个模型时，发现其中至少一个模型具有统计学意义显著的几率很高，但这可能是由于 Ⅰ 类错误。在测试多个模型时，总是调整显著性水平很重要，例如，使用Bonferroni校正。'''<font color = '#32cd32'>另外，不应该在同一数据集中进行探索性分析后进行验证性分析。one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. </font>'''
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| − | 探索性分析是用来为一个理论寻找想法，但不是用来检验这个理论的。当一个数据集中使用探索性分析发现了一个模型，然后在同一个数据集中进行验证性分析，这可能仅仅意味着验证性分析的结果首先是由于和探索性分析中同样的 Ⅰ 类错误而导致的。因此，验证性分析不会比最初的探索性分析更有用。
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| | + | 探索性分析是用来为一个理论寻找想法，但不是用来检验这个理论的。当一个数据集中使用探索性分析发现了一个模型，然后在同一个数据集中进行验证性分析，这可能仅仅意味着验证性分析的结果首先是由于和探索性分析中同样的 第'''Ⅰ'''类错误而导致的。因此，验证性分析不会比最初的探索性分析更有用。 |
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| | ====结果的稳定性==== | | ====结果的稳定性==== |