multiple comparisons across

USAGE SUMMARY

The phrase "multiple comparisons across" is correct and usable in written English.
It can be used in statistical contexts where you are discussing the analysis of multiple groups or variables simultaneously. Example: "The study conducted multiple comparisons across different age groups to determine the effectiveness of the treatment."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

To correct for multiple comparisons across the four different brain responses, FDR correction73 was applied.

Nature

Correction for multiple comparisons across time (stimulus onset until end of trial) was performed using FieldTrip's nonparametric cluster-based permutation method (1000 randomisations)19.

Nature

In particular, comparisons of classification accuracy time-series against chance were performed using t-tests at each time-point, correcting for multiple comparisons across time-points28.

Nature

Voxel-wise group-level analyses were carried out in SPM8 and, unless reported otherwise, used a FDR correction of P<0.05 for multiple comparisons across the whole-brain volume.

Nature

The standard alpha-level of p < 0.05 was used to determine significance, and cluster level p-values were corrected for multiple comparisons across all ROIs using the False Discovery Rate49.

Nature

For any given level of oxygen flow rate supplementation all differences between multiple comparisons across ventilators were statistically significant.

Intensive Care Medicine Experimental

For statistical analysis threshold-free cluster enhancement were used (TFCE) and corrected for multiple comparisons (across space) within the permutation framework.

The Journal of Headache and Pain

Third, while comparing the cephalometric values between DS and normative controls, we conducted multiple comparisons across age groups and within race and gender, increasing the potential for type 1 errors.

Progress in Orthodontics

A Westfall-Young approach [29] was used to adjust for the multiple comparisons across peptide pools.

Plosone

Significance levels were calculated taking into account the probability of a false detection for any given cluster [63], thereby correcting for multiple comparisons across all voxels.

Plosone

T-statistics for each voxel were thresholded at p<0.05 corrected for multiple comparisons across whole brain with a family wise error rate (FWE).

Plosone

Expert writing Tips

Best practice

When conducting statistical analyses involving "multiple comparisons across" different groups or variables, always apply appropriate correction methods (e.g., Bonferroni, FDR) to control for the increased risk of type I errors.

Common error

A common mistake is to perform "multiple comparisons across" different groups without adjusting the significance level. This inflates the probability of false positives, leading to incorrect conclusions. Always apply a correction method.

Linguistic Context

The phrase "multiple comparisons across" functions primarily as a descriptive term in statistical analysis. It's used to identify scenarios where numerous statistical tests are conducted, as evidenced by Ludwig's examples, which frequently discuss correction methods like FDR and Bonferroni to manage the increased risk of false positives in such situations. The Ludwig AI confirms that phrase is correct and usable in written English.

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, the phrase "multiple comparisons across" is a statistically-focused term that describes a scenario where numerous statistical tests are conducted on a single dataset. As Ludwig AI confirms, the phrase is correct and usable in written English. This situation requires careful attention to error control, often addressed through methods like Bonferroni or FDR correction. Its primary function is descriptive, alerting readers to the need for cautious interpretation of results. The term is mainly used in formal and scientific contexts, highlighting its technical nature. By understanding the context and purpose, writers can effectively use "multiple comparisons across" to accurately convey analytical challenges and methodological rigor in their work.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

multiple comparisons between

Changes the preposition from 'across' to 'between', emphasizing comparisons linking distinct items or groups.

repeated comparisons across

Replaces 'multiple' with 'repeated', highlighting the iterative aspect of the comparison process.

various comparisons across

Uses 'various' instead of 'multiple' to indicate a diverse set of comparisons.

numerous comparisons across

Replaces 'multiple' with 'numerous', emphasizing the large quantity of comparisons.

several comparisons across

Employs 'several' in place of 'multiple', denoting more than two comparisons, but not a particularly large number.

many comparisons across

Substitutes 'multiple' with 'many', indicating a large number of comparisons without specifying an exact quantity.

multiple assessments across

Changes 'comparisons' to 'assessments', suggesting a broader evaluation process rather than a direct comparison.

extensive comparisons across

Replaces 'multiple' with 'extensive', stressing the thoroughness and comprehensiveness of the comparisons.

a range of comparisons across

Uses 'a range of' instead of 'multiple' to indicate a variety of comparisons.

different comparisons across

Substitutes 'multiple' with 'different', highlighting the diversity in types of comparison.

FAQs

What does "multiple comparisons across" mean in statistics?

In statistics, "multiple comparisons across" refers to the situation where several statistical tests are conducted on the same dataset. This increases the chance of finding a statistically significant result purely by chance, requiring adjustments to maintain the desired significance level.

Why is correction needed when performing "multiple comparisons across" groups?

Correction methods are necessary because the probability of making at least one Type I error (false positive) increases with the number of comparisons. Without correction, the reported p-values may be misleadingly low, leading to false conclusions.

Which methods can be used to correct for "multiple comparisons across" datasets?

Common correction methods include Bonferroni correction, False Discovery Rate (FDR), Tukey's HSD, and Scheffé's method. The choice of method depends on the specific research question and the desired balance between controlling Type I and Type II errors.

What are some alternatives to "multiple comparisons across" in scientific writing?

Alternatives include "multiple comparisons between", "repeated comparisons across", or "various comparisons across", depending on the nuances you want to emphasize in your writing.