spurious data

USAGE SUMMARY

The phrase "spurious data" is correct and usable in written English.
It can be used to refer to data that is false, misleading, or not genuine, often in the context of research or data analysis. Example: "The results of the experiment were skewed due to the presence of spurious data that was not accounted for."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

51 human-written examples

Spurious data, they suggest, have also deterred proper regulation.

The New York Times

Remarks about "spurious data entry and analysis" are critical too; we really have to start looking at what data matters and what information we glean from it.

The Guardian

The pulmonary artery catheter, used to measure pressure in the heart and lungs, can yield spurious data because of poor positioning.

The New York Times - Health

Nine of the 13 root servers that guide traffic around the system were flooded with a deluge of spurious data intended to bring services to a halt.

The Economist

In that response, Dr. Sherwood, a retired ophthalmologist who was active in the Jackson hospital's union, said the investigators' conclusion that the radiologist had spent enough time reviewing images was based on "spurious" data.

The New York Times

Both elements 116 and 118 have vanished along with the spurious data.

Science Magazine

Human-verified similar examples from authoritative sources

Similar Expressions

9 human-written examples

Its prototype implementation, written in C++, has an autonomous self-identification mechanism to avoid spurious data-inflation in a publication-oriented data model.

Biological Journal of the Linnean Society

Creative industries that, despite these spurious losses (data from the Official Charts Company shows that sales of single tracks in 2009 have now surpassed the previous all-time record of 115.1m, set in 2008), can afford lobbyists in the finest Saville Row suits.

The Guardian - Opinion

These are similarly spurious: the data sets are sparse and simple, the outputs binary or highly limited.

TechCrunch

The initially spurious XPS data were re-analysed on the basis of a model developed from the AFM measurements.

Surface and Coatings Technology

The work of Chen, Song, and Xin [32] is an example of a solution that uses more complex statistics for detecting spurious sensing data in cooperative sensing.

EURASIP Journal on Information Security

Expert writing Tips

Best practice

When analyzing data, always implement robust quality control measures to identify and remove "spurious data" to ensure accurate results.

Common error

A common mistake is to overlook outliers in datasets, assuming they're all "spurious data". Investigate outliers carefully; they might reveal important insights instead of being mere errors.

Linguistic Context

The phrase "spurious data" functions as an adjective-noun combination, where "spurious" modifies "data". Ludwig confirms its grammatical correctness and usability. The adjective "spurious" indicates that the data is not genuine, valid, or true.

Expression frequency: Very common

Frequent in

Science

64%

News & Media

32%

Formal & Business

2%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "spurious data" is a common and grammatically correct term used to describe data that is false, misleading, or not genuine. Ludwig AI confirms that this phrase is suitable for both formal and neutral contexts, frequently appearing in scientific and news publications. Understanding and identifying "spurious data" is crucial in data analysis and research to ensure accuracy and validity of results. Common alternatives include "false data", "invalid data", and "erroneous data". It's essential to implement robust quality control measures but be cautious about dismissing outliers without proper investigation. Ignoring data outliers can lead to overlooking important insights. By addressing and mitigating the impact of "spurious data", researchers and analysts can enhance the reliability of their findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

false data

This alternative directly indicates the data is untrue or incorrect.

invalid data

This term suggests the data does not meet the required criteria or standards.

erroneous data

This indicates the data contains errors or inaccuracies.

incorrect data

This suggests that the data is factually wrong.

inaccurate data

This implies the data does not provide a precise or correct measurement or description.

unreliable data

This suggests the data cannot be trusted or depended upon.

flawed data

This indicates the data has defects or shortcomings.

faulty data

Similar to flawed data, this suggests the data contains errors due to a malfunction or defect.

misleading data

This means the data presents a false or distorted picture.

deceptive data

This suggests the data is intentionally presented to mislead or deceive.

FAQs

How can I identify "spurious data" in a dataset?

Identifying "spurious data" often involves looking for outliers, inconsistencies, or values that don't align with expected patterns. Statistical methods and domain knowledge can help pinpoint problematic data points.

What are some alternatives to saying "spurious data"?

You can use alternatives like "false data", "invalid data", or "erroneous data" depending on the specific context.

What is the difference between "spurious data" and "missing data"?

"Spurious data" refers to data points that are incorrect or misleading, while "missing data" indicates the absence of data where it should exist. They require different handling in data analysis.

How does "spurious data" affect the validity of research results?

"Spurious data" can skew results, leading to inaccurate conclusions. Removing or correcting such data is crucial for ensuring the validity and reliability of research findings.