multiple comparisons test

USAGE SUMMARY

'multiple comparisons test' is a correct and usable phrase in written English.
You can use it when referring to a statistical test used to compare the means of two or more groups. For example, "The results of the multiple comparisons test indicated a significant difference in the means between the two groups."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Statistical analysis was performed using one-way ANOVA with Dunnett's multiple comparisons test.

Nature

Statistical analysis was performed by two-way ANOVA with Dunnett's multiple comparisons test.

Nature

***p < 0.001, one-way ANOVA post hoc Holm-Sidak's multiple comparisons test.

Nature

Statistical differences were analysed by one-way analysis of variance (ANOVA) with Tukey's multiple comparisons test.

Nature

Variation in means were tested for significance using Dunnett's multiple comparisons test.

Nature

One-way ANOVA with repeated measures followed by Bonferroni multiple comparisons test (n = 5).

Nature

*p < 0.05, **p < 0.01, ***p < 0.001 compared to vector, one-way ANOVA post hoc Holm-Sidak's multiple comparisons test.

Nature

Probe trial latency and swim speed were analyzed by one-way ANOVA with Tukey's multiple comparisons test.

Nature

* indicates significant difference using Friedmans non-parametric paired test with Dunn's multiple comparisons test of all groups against control.

Nature

One-way analysis of variance (followed by Tukey's multiple comparisons test) was used to compare differences among multiple groups.

Nature

Endothelial cell branch point assay was analysed by paired one-way analysis of variance with Tukey's multiple comparisons test.

Nature

Expert writing Tips

Best practice

When reporting results from a "multiple comparisons test", clearly state which specific test was used (e.g., Tukey's, Bonferroni, Dunnett's) and the alpha level for significance.

Common error

A common mistake is performing multiple statistical tests without adjusting the significance level. This increases the risk of false positives. Always use a "multiple comparisons test" or other appropriate correction method when comparing more than two groups.

Linguistic Context

The phrase "multiple comparisons test" functions as a noun phrase that identifies a specific type of statistical test. It is used to refer to a statistical procedure, as illustrated by the Ludwig examples that describe its application and interpretation.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "multiple comparisons test" is a cornerstone of statistical analysis, particularly in scientific research. Ludwig's AI confirms its grammatical correctness and prevalence in academic and scientific contexts. When writing about statistical analyses, it's crucial to specify the exact test used (e.g., Tukey's, Bonferroni) and understand the underlying principles of familywise error rate control. Common pitfalls include neglecting to correct for multiple comparisons, leading to inflated false positive rates. For alternative phrases, you can consider "post-hoc analysis" or "pairwise comparison test". In essence, mastering the "multiple comparisons test" involves both proper application and clear communication of the statistical methods employed.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

test for multiple comparisons

This is a minor variation in wording, emphasizing the purpose of the test.

post-hoc analysis

This alternative refers to the statistical tests applied after ANOVA to determine where differences exist between groups.

multiplicity control

This is a general term for methods that address the problems arising from performing multiple statistical tests.

pairwise comparison test

This alternative focuses on comparing pairs of groups to identify significant differences.

significance testing correction

This emphasizes the need to adjust significance levels to account for multiple comparisons.

adjustment for multiple testing

This phrase describes the process of correcting p-values when performing multiple statistical tests.

simultaneous inference

This refers to the process of drawing conclusions about multiple parameters or hypotheses at the same time, accounting for dependencies.

familywise error rate control

This alternative refers to methods that control the probability of making one or more false discoveries when performing multiple hypothesis tests.

protected t-tests

This term describes the use of t-tests only after a significant ANOVA result, providing some protection against inflated Type I error.

statistical contrasts

This alternative covers a broader range of tests used for comparing specific groups or combinations of groups.

FAQs

What is the purpose of a "multiple comparisons test"?

A "multiple comparisons test" is used to determine which groups differ significantly from each other after an ANOVA test indicates an overall significant difference. It helps control the familywise error rate, reducing the chance of false positives.

Which "multiple comparisons test" should I use?

The choice of "multiple comparisons test" depends on the specific research question and the characteristics of the data. Common options include Tukey's HSD, Bonferroni, Dunnett's, and Scheffe's tests. Consult statistical resources to determine the most appropriate test for your situation.

What are some alternatives to using a "multiple comparisons test"?

Alternatives to using a "multiple comparisons test" include controlling the false discovery rate (FDR) using methods like the Benjamini-Hochberg procedure, or focusing on planned comparisons with a limited number of pre-specified hypotheses. Another approach is to use "pairwise comparison tests".

How do I report the results of a "multiple comparisons test" in a research paper?

When reporting the results of a "multiple comparisons test", include the name of the test used, the test statistic, degrees of freedom, p-value, and effect size (if applicable). Also, clearly indicate which groups were found to be significantly different from each other. The APA style guidelines offer comprehensive explanations on reporting the results.