data significance

USAGE SUMMARY

The phrase "data significance" is correct and usable in written English.
It can be used when discussing the importance or relevance of data in a particular context, such as research, analysis, or decision-making. Example: "The data significance of these findings cannot be overstated, as they provide crucial insights into consumer behavior."

✓ Grammatically correct

Science

Formal & Business

News & Media

Human-verified examples from authoritative sources

Exact Expressions

21 human-written examples

When required, data significance was assessed with Student's t test; P < 0.05 was considered significant.

Arthritis Research and Therapy

This provides data significance and drives the data transfer process through industrial partners along the supply chain.

IFAC Proceedings Volumes

Wilcoxon test was performed to analyze data significance.

Plosone

For normally distributed data, significance was assessed with the Student t-test.

Plosone

While lowess curves provide a visual representation of the patterns in the data, significance of interactions was determined by the p-values in the multiple linear regression.

Plosone

To reduce the variability of each patient sample and to evaluate the data significance, we generated synthetic referring gels from pH 3 10 2D maps of each subject category by the GE 2D Image Master Platinum software analysis.

Plosone

Human-verified similar examples from authoritative sources

Similar Expressions

39 human-written examples

After hybridization and data normalization, significance analysis of microarrays (SAM) software was used to determine significant differences in gene expression.

BMC Cancer

Even when the background probabilities are well understood, it is possible to wrangle the data until significance appears or disappears to taste.

The Guardian - Opinion

At a congressional hearing last week, Richard Glenn, an executive with the Arctic Slope Regional Corporation, one of the Native partners, played down the data's significance.

The New York Times

Morley published his reading of the date in the Initial Series Building (Morley 1918), but otherwise neither of them recorded data of significance.

Harvard University

This time, a new event was recorded, but instead of having 1-2 secofdataf dathe the significance lasted over a minute.

Forbes

Expert writing Tips

Best practice

When discussing "data significance", specify the statistical test used to determine significance (e.g., t-test, ANOVA) and the p-value threshold.

Common error

Avoid overstating the "data significance" without proper statistical validation. Always provide context and limitations of the data's interpretation.

Linguistic Context

The phrase "data significance" functions as a noun phrase, typically used to denote the importance or relevance of data within a specific context. Ludwig examples show that this phrase is commonly used within scientific research to highlight statistically significant findings or to assess the impact of certain data sets.

Expression frequency: Uncommon

Frequent in

Science

80%

News & Media

10%

Formal & Business

10%

Less common in

Academia

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, the phrase "data significance" is a noun phrase primarily used to emphasize the importance or relevance of data, particularly in scientific and research contexts. Ludwig AI confirms that it is grammatically correct and acceptable in written English. While its frequency is uncommon, it is a crucial term in statistical analysis and research interpretation. Alternatives include "statistical significance" and "importance of data". When using this phrase, be precise about the statistical methods and p-values involved to avoid overstating the findings. The common mistake to avoid is presenting "data significance" without sufficient validation. Always provide context and limitations in interpretations.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

statistical significance

Focuses specifically on the statistical aspect of significance, implying a rigorous quantitative assessment.

importance of data

Replaces "significance" with "importance", offering a more direct and common way to express the relevance of data.

relevance of data

Similar to "importance of data", but emphasizes the pertinence and applicability of the data to a specific context.

value of data

Highlights the worth or utility of the data in a particular situation or analysis.

impact of data

Emphasizes the effect or influence that the data has on a particular outcome or understanding.

meaning of data

Shifts the focus to the interpretative aspect, emphasizing what the data reveals or suggests.

data's implication

Highlights what the data suggests or hints at, particularly in terms of future consequences or understandings.

data's weight

Suggests the degree of influence or credibility that the data carries in decision-making or analysis.

data's noteworthiness

Indicates the degree to which the data merits attention or consideration due to its unusual or impactful nature.

data's consequentiality

Stresses the far-reaching effects or results that the data can lead to, often implying substantial implications.

FAQs

How do I assess "data significance" in research?

Assessing "data significance" typically involves statistical tests. The choice of test depends on your data type and research question. A p-value less than a predetermined significance level (often 0.05) indicates statistical significance.

What's the difference between "statistical significance" and practical significance?

"Statistical significance" indicates whether an effect is likely due to chance. Practical significance refers to whether the effect is meaningful in the real world. A statistically significant result may not always be practically significant, and vice versa.

What are some alternatives to the phrase "data significance"?

You can use alternatives such as "importance of data", "relevance of data", or "statistical significance" depending on the context.

How does sample size affect "data significance"?

Larger sample sizes generally increase the power of statistical tests, making it easier to detect significant effects. Small sample sizes may fail to detect real effects, while very large samples can produce statistically significant results even for trivial effects.