multiple testing correction

USAGE SUMMARY

The phrase "multiple testing correction" is correct and usable in written English.
It is commonly used in the field of statistics and research to refer to the practice of adjusting statistical results for conducting multiple hypothesis tests. Example: The researchers used a Bonferroni correction to account for the problem of multiple testing, ensuring the accuracy of their results.

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

bAdjusted P-values was corrected of nominal P-values by Benjamini-Hochberg multiple testing correction.

BMC Genomics

P values were corrected using the Benjamini and Hochberg false discovery rate multiple testing correction procedure.

BMC Genomics

This approach requires rigorous multiple testing correction.

Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health

False discovery rate (FDR) multiple testing correction was also performed.

Sports Medicine - Open

Methods for multiple testing correction and their application are described.

Methods

Multiple testing correction analysis was not applied, and all unassigned reads were ignored.

AMB Express

Multiple testing correction was performed using the Benjamini-Hochberg algorithm.

Plosone

The target gene set of miR-181a remained significant after multiple testing correction (FDR = 0.048).

Plosone

The authors thank Dr. Sabina Bijlsma (TNO, the Netherlands) for help with multiple testing correction analysis.

Plosone

The analysis was performed assuming a hypergeometric distribution and using the Bonferroni multiple testing correction.

Plosone

Key statistical and multiple testing correction methods used by each tool are shown in Table S8.

Plosone

Expert writing Tips

Best practice

When reporting statistical results, always specify which "multiple testing correction" method was applied (e.g. Bonferroni, Benjamini-Hochberg) for transparency and reproducibility.

Common error

Failing to apply a "multiple testing correction" when conducting multiple statistical tests can lead to an inflated rate of false positives, making your findings unreliable.

Linguistic Context

The phrase "multiple testing correction" functions as a noun phrase that describes a statistical procedure. Ludwig AI confirms its correctness and usability. It's used to refer to the methods employed to adjust p-values in studies involving multiple hypothesis tests.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

In summary, "multiple testing correction" is a crucial statistical technique used to address the problem of inflated false positive rates when conducting multiple hypothesis tests. Ludwig AI validates its grammatical correctness and its frequent usage in scientific literature. Common methods include Bonferroni, Benjamini-Hochberg, and Sidak corrections. It's vital to specify the method used when reporting results. Failing to apply a "multiple testing correction" can lead to unreliable conclusions. Always remember to adjust your statistical analyses when performing multiple comparisons to ensure the integrity of your research.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

adjustment for multiple comparisons

Focuses on the act of adjusting results rather than the overall process of correction.

multiple comparisons adjustment

Reorders the words for a slightly different emphasis, but retains the core meaning.

correction for multiple comparisons

Swaps "testing" with "comparisons", highlighting the comparison aspect of the tests.

false discovery rate control

Specifies the goal of the correction, which is to control the false discovery rate.

family-wise error rate control

Highlights a specific method of controlling error rates across multiple tests.

statistical significance adjustment

Broadens the concept to any adjustment made to statistical significance due to multiple tests.

p-value adjustment

Refers specifically to adjusting p-values to account for multiple comparisons.

controlling type I error

Emphasizes the reduction of type I errors (false positives) as the purpose of the correction.

significance level correction

Addresses the adjustment of the significance level to maintain accuracy.

Bonferroni correction

Names a specific, common method used for multiple testing correction.

FAQs

What is "multiple testing correction" and why is it necessary?

"Multiple testing correction" refers to statistical methods used to adjust p-values when performing multiple hypothesis tests. It's necessary to control the false discovery rate and avoid incorrectly concluding that effects are significant.

What are some common methods for "multiple testing correction"?

Common methods include the "Bonferroni correction", "Benjamini-Hochberg procedure" (FDR control), and "Sidak correction". The choice depends on the desired balance between controlling false positives and false negatives.

How does "multiple testing correction" affect p-values?

"Multiple testing correction" typically makes it more difficult to achieve statistical significance by raising the threshold that a p-value must cross. This adjustment reduces the likelihood of false positives.

When should I use "multiple testing correction"?

You should use "multiple testing correction" whenever you are conducting multiple statistical tests on the same dataset. This is especially important in exploratory analyses where many comparisons are made.