Undergrad 'Conservative' p-values adjusted

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SUMMARY

The discussion centers on strategies for adjusting p-values to control for Type 2 errors, particularly in the context of high false negative rates. Participants emphasize the importance of conducting power calculations prior to data collection to avoid post-hoc statistical errors. While traditional methods focus on Type 1 error control, the need for Type 2 error management is highlighted as crucial for accurate statistical inference.

PREREQUISITES
  • Understanding of p-value adjustments and their implications
  • Familiarity with Type 1 and Type 2 errors in hypothesis testing
  • Knowledge of power calculations in statistical analysis
  • Experience with statistical software for data analysis
NEXT STEPS
  • Research power calculation techniques for hypothesis testing
  • Explore methods for adjusting p-values to control Type 2 errors
  • Learn about the implications of post-hoc power analysis
  • Investigate statistical software options for conducting power analyses
USEFUL FOR

Statisticians, data analysts, researchers conducting hypothesis testing, and anyone interested in improving the accuracy of statistical conclusions.

Schwann
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How to adjust p-values in such a way that they become more 'liberal'?
Hello everyone!

Could anybody recommend some strategy of p-values adjustment, as the distribution of my p-values indicates the presence of a big number of false negatives? Usually p-values are adjusted in order to overcome Type 1 errors (e. g. FDR or FWER estimation), but what I need to do is to control for Type 2 errors.

Any thoughts would be appreciated!
 
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You need to do a power calculation. That will give you the information you need. However, be aware that doing so post-hoc is a statistical mistake. This analysis should be done before collecting data.
 
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