multiple comparisons tests

USAGE SUMMARY

The phrase “multiple comparisons tests” is correct and usable.
You can use it when referring to statistical tests that analyze more than two sets of data. For example: “We used multiple comparisons tests to evaluate the differences between the three groups.”.

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

49 human-written examples

Statistical analysis by Kruskal-Wallis and Dunn's Multiple Comparisons tests demonstrated that expression in the precocious testes was significantly different from that in the non-precocious testes for all genes (P < 0.001; beta-globin beta P < 0.01) as it was also for July v November maturing adults.

BMC Genomics

Difference between means were tested by one-way ANOVA and LSD multiple comparisons tests.

Botanical Studies

For A and B, the displayed letters; a, b and c indicate the significance (P < 0.05; one-way ANOVA and Tukey's multiple comparisons tests).

Botanical Studies

Significant differences among treatments were analyzed by one-way ANOVA, using p ≤ 0.05 as a significant level and Tukey-Kramer multiple comparisons tests conducted for pair wise comparisons between treatments.

Botanical Studies

Even though none of the multiple comparisons tests performed were significant, a trend analysis shows that the percentage of selections using CHEX increased linearly across the four test scenarios, F 1, 18) = 6.286, p = 0.022, ηp 2 = 0.259, suggesting that participants began to see the benefit in using the tool the more they became familiar with it.

Cognitive Research: Principles and Implications

and consisted of one- or two-way ANOVA followed by Tukey's multiple comparisons tests.

Plosone

Human-verified similar examples from authoritative sources

Similar Expressions

11 human-written examples

Dunn's multiple comparisons test was used as a post hoc test for intergroup comparisons.

Intensive Care Medicine Experimental

Data were analyzed using Tukey-Kramer multiple comparisons test.

Plosone

To adjust for multiple comparisons testing, we fixed the α error to 1%.

Plosone

Significant difference in the ISCK03 effect was determined by Tukey's HSD multiple comparisons test.

Plosone

ANOVA/Bonferroni multiple comparisons test, N = 4 6.

eLife

Expert writing Tips

Best practice

When reporting results, clearly specify which "multiple comparisons tests" were used (e.g., Tukey's, Bonferroni, Dunn's) to ensure reproducibility and transparency in your statistical analysis.

Common error

A common error is not adjusting the alpha level when conducting "multiple comparisons tests". This can inflate the Type I error rate (false positive), leading to incorrect conclusions. Remember to apply a correction method appropriate for your data and comparisons.

Linguistic Context

The phrase "multiple comparisons tests" functions as a noun phrase referring to a category of statistical tests used to determine which groups differ significantly from each other after an analysis of variance (ANOVA) or similar statistical procedure. Ludwig AI confirms the correctness of the phrase.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

The phrase "multiple comparisons tests" is a commonly used term in scientific and statistical contexts to describe a range of procedures used to identify significant differences between multiple groups. Ludwig AI confirms the phrase is grammatically sound and frequently used. The choice of specific test (e.g., Tukey's, Bonferroni, Dunn's) depends on the experimental design and data characteristics. Remember to clearly report the specific test used and adjust alpha levels appropriately to avoid inflated Type I error rates. Alternatives like "post hoc tests" and "pairwise comparisons" can also be used, but "multiple comparisons tests" is a comprehensive and widely understood term.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

post hoc tests

Focuses on the 'after-the-fact' nature of these tests, performed after an ANOVA.

pairwise comparisons

Emphasizes the comparison of all possible pairs of groups.

multiple testing correction

Highlights the adjustment needed due to performing multiple tests.

adjusted p-values

Focuses on the outcome of the correction process applied to p-values.

significance testing with correction

More general term including several method for corrected significance

tests for group differences

Broader category referring to any test identifying differences between groups.

statistical tests for multiple groups

Highlights the use of statistical methods for analyzing multiple groups.

methods for controlling type i error

Specifically addresses controlling the false positive rate in multiple tests.

correction for inflated alpha

Focuses on the alpha level adjustment in the tests.

statistical contrasts

Implies a set of planned comparisons among treatment means.

FAQs

How do I choose the right "multiple comparisons test" for my data?

The choice depends on your experimental design, the type of data, and the specific comparisons you want to make. Common tests include Tukey's HSD, Bonferroni correction, Dunn's test, and Scheffé's method. Consult a statistician or relevant resources for guidance.

What is the Bonferroni correction in "multiple comparisons tests"?

The Bonferroni correction is a conservative method that adjusts the significance level (alpha) by dividing it by the number of comparisons being made. While simple, it can be overly conservative, potentially increasing the Type II error rate (false negative).

How do post-hoc tests relate to "multiple comparisons tests"?

Post-hoc tests are "multiple comparisons tests" performed after a significant result is obtained in an ANOVA (analysis of variance). They help determine which specific groups differ significantly from each other.

What alternatives can I use to describe similar statistical processes besides "multiple comparisons tests"?

You could use terms like "post hoc tests", "pairwise comparisons", or "multiple testing correction", depending on the context and the specific aspect you want to emphasize.