extraneous data

USAGE SUMMARY

The phrase "extraneous data" is correct and usable in written English.
It can be used to refer to information that is irrelevant or not essential to the main topic or purpose. Example: "In our analysis, we found that the extraneous data did not contribute to our findings and could be omitted."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

21 human-written examples

"They're more likely to compartmentalize, get rid of extraneous data and proceed in a straightforward path.

The New York Times

Such extraneous data might include large numbers of minors, details of earnings or medical information".

The Guardian

"In addition, NSA actively works to remove extraneous data, to include that of innocent foreign citizens, as early as possible in the process".

The Guardian

"NSA actively works to remove extraneous data, to include that of innocent foreign citizens, as early as possible in the process".

Independent

Almost every one -- in Cordoba, the Alhambra in Granada, the dramatic mountain town of Ronda, the sherry winery in Jerez de la Frontera, and Seville -- provided just the right amount of information, without suffocating us with extraneous data.

The New York Times - Travel

Similarly, personal information like one job seeker's description of himself as a "single, white male" (which prompted the hiring manager to ask, "Am I supposed to place him or date him?") and another's boast that he loved to play with his "17 children that resulted from 9 marriages" argues strongly for eliminating extraneous data.

The New York Times

Human-verified similar examples from authoritative sources

Similar Expressions

38 human-written examples

Data were amended to remove extraneous information, and multiple email identities were standardized for consistency.

International Journal of STEM Education

Choose the Clean Up Settings Panel to remove extraneous, useless data from your document.

WikiHow

Eliminate extraneous information from the parenthetical citation.

WikiHow

With the current amount of data, extraneous peaks should pop up, Tonelli says.

Science Magazine

It is to construct a group from a set solely on the basis of the concept of group and nothing else, i.e., with no extraneous relation or data.

SEP

Expert writing Tips

Best practice

When processing large datasets, identify and remove "extraneous data" early to improve efficiency and reduce the risk of analysis errors.

Common error

Avoid accidentally discarding relevant information when cleaning data. Always verify that data marked as "extraneous data" truly does not contribute to your analysis or understanding of the subject.

Linguistic Context

The phrase "extraneous data" functions as an adjective-noun combination. "Extraneous" modifies "data", indicating that the data is irrelevant or unnecessary. As Ludwig AI confirms, this construction is grammatically sound and commonly used.

Expression frequency: Uncommon

Frequent in

Science

48%

News & Media

36%

Wiki

8%

Less common in

Formal & Business

0%

Encyclopedias

0%

Social Media

0%

Ludwig's WRAP-UP

In summary, "extraneous data" is a grammatically correct and usable phrase that refers to irrelevant or unnecessary information. As Ludwig AI highlights, it's most frequently encountered in scientific and news contexts. The phrase serves to identify and categorize non-essential information. Common alternatives include "irrelevant information" and "unnecessary data". When using this phrase, ensure you're not accidentally discarding essential data. By removing "extraneous data", you can improve efficiency in your analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

irrelevant information

This alternative uses a different adjective to describe the information, emphasizing its lack of relevance.

unnecessary data

This alternative uses a different adjective to describe the data, highlighting that it is not needed.

redundant information

This alternative emphasizes that the information is repetitive and adds no new value.

superfluous data

This alternative uses a more formal term, "superfluous", to indicate that the data is excessive.

immaterial information

This alternative underscores the lack of consequence or importance of the information.

non-essential data

This alternative directly contrasts the data with what is essential or required.

peripheral data

This alternative suggests that the data is on the edge of the main topic and not central to it.

excess data

This alternative highlights that there is too much data present.

extraneous details

This alternative replaces "data" with "details", focusing on the specific pieces of information.

unrelated information

This alternative emphasizes a lack of connection between the information and the subject at hand.

FAQs

How can I identify "extraneous data" in a research project?

Identify "extraneous data" by determining which information is not essential for answering your research question or supporting your hypothesis. Focus on data directly relevant to your core objectives.

What is the difference between "extraneous data" and "irrelevant information"?

"Extraneous data" refers to information that is not essential or necessary, while "irrelevant information" is not related or applicable to the matter at hand. They are similar, but "extraneous data" may still have some tangential connection, whereas irrelevant information has none.

What are some strategies for eliminating "extraneous data"?

Strategies for eliminating "extraneous data" include setting clear inclusion/exclusion criteria, using data cleaning techniques to remove duplicates or errors, and focusing on variables directly relevant to your analysis. Regularly audit your data to ensure only necessary information is retained.

Is it always necessary to remove "extraneous data"?

While removing "extraneous data" is often beneficial for clarity and efficiency, it's not always necessary. In some cases, keeping additional context or background information may provide a more complete picture, as long as it doesn't obscure the main findings.