multiple testing

USAGE SUMMARY

The phrase "multiple testing" is correct and usable in written English.
It is typically used in statistical contexts to refer to the situation where multiple hypotheses are tested simultaneously, which can increase the chance of false positives. Example: "In our study, we conducted multiple testing to evaluate the effectiveness of the new drug across various patient demographics."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

P-values were adjusted to multiple testing by Bonferroni correction.

Nature

A statistical approach that controls for multiple testing.

Nature

P values were not corrected for multiple testing.

Nature

However, no comparison survived correction for multiple testing.

Nature

Therefore, no correction for multiple testing was done.

Nature

This approach requires rigorous multiple testing correction.

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

No adjustment for multiple testing was performed.

The Journal of Headache and Pain

We did not correct for multiple testing.

The Journal of Headache and Pain

No multiple testing adjustments were applied.

EJNMMI Research

No correction for multiple testing was included.

EJNMMI Research

Permutation analysis was used to account for multiple testing.

Duke University

Expert writing Tips

Best practice

When reporting results from analyses involving "multiple testing", clearly state the method used to control for the increased risk of false positives (e.g. Bonferroni correction, Benjamini-Hochberg procedure).

Common error

A common mistake is to perform multiple statistical tests without adjusting the significance level. This can lead to an inflated rate of false positive findings. Always consider the need for adjustment when conducting "multiple testing".

Linguistic Context

The phrase "multiple testing" functions as a noun phrase and a statistical term. It refers to the act of performing multiple statistical tests simultaneously. As Ludwig AI confirms, it is a common term, particularly in scientific research.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, "multiple testing" is a frequently encountered term in statistical analysis, particularly within scientific research. Ludwig AI confirms its usability and correctness. It describes the act of conducting multiple statistical tests, emphasizing the need to address the increased risk of false positives. Appropriate correction methods, such as Bonferroni or FDR control, are crucial for maintaining the reliability of research findings. Failing to account for "multiple testing" is a common error that can lead to inaccurate conclusions. Therefore, awareness and proper handling of "multiple testing" are essential for sound statistical practice.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

simultaneous hypothesis testing

This alternative emphasizes the concurrent nature of the testing process.

multiple comparisons problem

This alternative highlights the problem that arises when making multiple statistical comparisons.

family-wise error rate control

This alternative focuses on controlling the probability of making one or more false discoveries.

multiplicity adjustment

This alternative refers to adjusting statistical significance to account for multiple tests.

false discovery rate control

This alternative emphasizes controlling the expected proportion of false discoveries among rejected hypotheses.

simultaneous inference

This alternative focuses on making inferences about multiple parameters at the same time.

repeated measures analysis

This alternative refers to analyzing data where the same variable is measured multiple times on each subject.

global testing

This alternative implies a comprehensive assessment across multiple factors.

joint significance testing

This alternative describes assessing the overall significance of a set of hypotheses.

composite hypothesis testing

This alternative involves testing a hypothesis that encompasses multiple components.

FAQs

What is "multiple testing" in statistics?

"Multiple testing" refers to the situation where several statistical tests are performed on the same dataset. This increases the probability of finding a statistically significant result by chance, even if no true effect exists.

Why is correction for "multiple testing" necessary?

Correction for "multiple testing" is crucial to control the false positive rate. Without it, researchers risk drawing incorrect conclusions from their data, leading to unreliable or irreproducible findings.

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

Common methods include Bonferroni correction, which adjusts the significance level by dividing it by the number of tests, and False Discovery Rate (FDR) control, such as the Benjamini-Hochberg procedure, which controls the expected proportion of false discoveries.

When is it appropriate to skip adjustment for "multiple testing"?

Skipping adjustment is rarely appropriate in confirmatory research. It might be considered in purely exploratory analyses, but any findings should be interpreted with extreme caution and validated in independent datasets. An alternative option could be "exploratory data analysis".