Fitting Linear Models • Statistical Details • The Usual Assumptions • Multiple Inferences
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Multiple Inferences
Often the value of the statistical results is not that you believe in them directly, but rather that they provide a key to some discovery. To confirm the discovery, you may need to conduct further studies. Otherwise, you might just be sifting through the data.
For instance, if you conduct enough analyses you can find 5% significant effects in five percent of your studies by chance alone, even if the factors have no predictive value. Similarly, to the extent that you use your data to shape your model (instead of testing the correct model for the data), you are corrupting the significance levels in your report. The random error then influences your model selection and leads you to believe that your model is better than it really is.

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Help created on 9/19/2017