impartiality of data

USAGE SUMMARY

The phrase "impartiality of data" is correct and usable in written English.
It can be used when discussing the neutrality and objectivity of data in research, analysis, or reporting contexts. Example: "The impartiality of data is crucial for ensuring that the findings of the study are credible and unbiased."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified similar examples from authoritative sources

Similar Expressions

60 human-written examples

This will in-turn improve the impartiality of the data upon which care decisions are based.

Forbes

These decisions will depend on the type of intervention, stakeholders' expectations of the strength of the evidence and the impartiality of the assessor, data availability, limitations of existing analytic methods and the cost of assessment.

Health Policy and Planning

Fellowship evaluation interviews were conducted by fellowship hosts which may have compromised impartiality, however data collection, analysis and interpretation were undertaken in awareness of this.

BMC Medical Education

"The independence or impartiality of arbitrators may be compromised".

The Guardian

Independence / Neutrality / Impartiality - The ODR provider must be sufficiently independent from both the online merchant and the consumer in order to guarantee the impartiality of its actions.

Harvard University

Egypt's ambassador to Berlin said he was confident of the "impartiality" of German justice.

Independent

The impartiality of one of the two investigating magistrates, Claire Thépaut, was questioned by Sarkozy supporters.

Independent

Concerns have also been aired over the impartiality of David Trimble, one of the international observers.

Independent

They also have questioned the impartiality of its officers.

The New York Times

"The impartiality of the tax audit was complete".

The New York Times

"The agency has full confidence in the professionalism and impartiality of the inspectors concerned," it said.

The New York Times

Expert writing Tips

Best practice

When using the phrase "impartiality of data", ensure it is clear what processes or aspects are being evaluated for bias.

Common error

Don't assume that data collection or analysis is inherently impartial. Always critically evaluate the methods used to gather and interpret the data to ensure objectivity.

Linguistic Context

The phrase "impartiality of data" functions as a noun phrase, typically acting as the subject or object of a sentence. It denotes the quality of data being unbiased and objective. According to Ludwig, it refers to the neutrality and objectivity of data in various contexts.

Expression frequency: Missing

Frequent in

Science

33%

News & Media

33%

Formal & Business

33%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "impartiality of data" refers to the neutrality and objectivity of information, crucial for unbiased decision-making and analysis. Ludwig AI confirms its grammatical correctness and usability in written English. While examples of its usage are currently limited, alternatives such as "data neutrality", "objectivity in data", and "unbiased data" offer similar meanings. When employing the phrase, it's vital to evaluate data collection and analysis methods critically, ensuring objectivity and minimizing potential biases. Despite its current infrequency, understanding and applying the concept of "impartiality of data" is essential for maintaining fairness and accuracy in data-driven processes.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Absence of bias in data

Directly points out the lack of inclination or prejudice in the data.

Unbiased data

Emphasizes the absence of prejudice or favoritism in the data.

Data without prejudice

Stresses the lack of preconceived notions affecting the data.

Data neutrality

Focuses on the unbiased nature of the data itself.

Fairness in data

Highlights the equitable treatment and representation within the data.

Objectivity in data

Highlights the importance of unbiased data collection and interpretation.

Data free from discrimination

Highlights the lack of unjust or prejudicial treatment in data.

Neutral data handling

Emphasizes the unbiased procedures used when processing data.

Data integrity

Focuses on the accuracy and consistency of data, which is crucial for impartiality.

Equitable data representation

Focuses on balanced depiction and treatment in the data.

FAQs

How can I ensure the "impartiality of data" in my research?

To ensure the "impartiality of data", focus on employing rigorous data collection methods, using diverse data sources, and being transparent about any potential biases or limitations in your study. Also, consider consulting with experts in bias detection and mitigation.

What does "impartiality of data" mean in the context of machine learning?

In machine learning, "impartiality of data" means that the training data used to build models should not reflect or amplify existing societal biases. This requires careful data curation and consideration of fairness metrics when evaluating model performance.

What are some alternatives to saying "impartiality of data"?

You can use alternatives like "data neutrality", "objectivity in data", or "unbiased data" depending on the specific nuance you want to convey.

Why is "impartiality of data" important?

"Impartiality of data" is crucial because biased data can lead to unfair or discriminatory outcomes in various applications, such as healthcare, finance, and criminal justice. Ensuring data impartiality promotes fairness, equity, and social justice.