'Conservative' p-values adjusted

In summary, adjusting 'conservative' p-values is done to account for multiple comparisons in a study and reduce the chances of obtaining false positive results. This is done through methods such as the Bonferroni correction, Holm-Bonferroni method, and Benjamini-Hochberg procedure. These adjusted p-values should be used in studies with multiple hypothesis tests to decrease the likelihood of false positive results. However, they are not always necessary and using unadjusted p-values may be sufficient in some cases. Adjusted p-values may make it harder to detect significant results, but this trade-off is necessary to avoid false positives. The interpretation of study results should always consider the use of adjusted p-values and the potential for false positives.
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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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  • #2
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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Related to 'Conservative' p-values adjusted

1. What does it mean to have a 'conservative' p-value adjustment?

A 'conservative' p-value adjustment refers to a statistical method that takes into account multiple comparisons in a study, in order to reduce the likelihood of falsely claiming a significant result. This adjustment is typically used when conducting multiple statistical tests on the same data set, in order to control for the increased chance of obtaining a false positive.

2. How is a 'conservative' p-value adjustment calculated?

A 'conservative' p-value adjustment is typically calculated using methods such as the Bonferroni correction or the Holm-Bonferroni method. These methods adjust the significance level (alpha) of each individual test, in order to maintain an overall alpha level for the entire study.

3. When should a 'conservative' p-value adjustment be used?

A 'conservative' p-value adjustment should be used when conducting multiple statistical tests on the same data set, in order to avoid falsely claiming a significant result. This is especially important when the number of comparisons is large, as the chance of obtaining a false positive increases with each additional test.

4. Are there any limitations to using a 'conservative' p-value adjustment?

Yes, there are some limitations to using a 'conservative' p-value adjustment. This method may be overly conservative, meaning that it may reject potentially valid results. Additionally, it does not control for all types of errors, such as Type II errors, and may not be appropriate for certain types of data or study designs.

5. Can a 'conservative' p-value adjustment be used in all types of statistical tests?

Yes, a 'conservative' p-value adjustment can be used in most types of statistical tests, including t-tests, ANOVAs, and regression analyses. However, it may not be appropriate for all types of data or study designs, and should be carefully considered before use.

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