honest data

USAGE SUMMARY

The phrase "honest data" is correct and usable in written English.
You can use it when referring to data that is accurate, truthful, and reliable, often in discussions about research, statistics, or information integrity. Example: "In order to make informed decisions, we must rely on honest data that reflects the true situation."

✓ Grammatically correct

News & Media

Science

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

7 human-written examples

"Honest data would have earned Claremont McKenna the No. 18 ranking.

The New York Times

"For someone like me who has long campaigned for open and honest data in public life I could not have set foot on a battle bus that has at the heart of its campaign a figure that I know to be untrue," said Wollaston, who is a former GP.

The Guardian

The push that became a shove was, the former GP says, the claim by Vote Leave that quitting would free up £350m a week for the NHS – something she says "simply isn't true": For someone like me who has long campaigned for open and honest data in public life I could not have set foot on a battle bus that has at the heart of its campaign a figure that I know to be untrue.

The Guardian

Confidentiality and intellectual property protection in such a competitive phase are ensured by an "honest data broker" data management system.

Comprehensive Medicinal Chemistry III

It's easier to get real, honest data through an independent third party than by asking the customer yourself.

Forbes

"The way the bitcoin algorithm works, if 99 out of 100 connections to your [ground-based bitcoin machine] are malicious, that one honest connection will be discovered and honest data chosen over others," Garzik told VICE News.

Vice

Human-verified similar examples from authoritative sources

Similar Expressions

52 human-written examples

In order to gain more reliable data, before the interview, the local FWM and the enumerator encouraged the client to provide honest responses.

BMC Women's Health

You can also fill out your profile, but to be honest the data points are so scant that I doubt they'd be able to "match individuals on several factors, including level of attraction, type of interest, and their smell".

TechCrunch

These proportions were similar regardless of the order of methods, with the exception that no woman who took ACASI after FtF believed that ACASI would provide less honest answers (data not shown).

Plosone

"What was supposed to be an open, honest and data-driven debate about drug policies has turned into a narrowly conceived closed-door affair," they wrote.

The Guardian

What was supposed to be an open, honest and data-driven debate about drug policies has turned into a narrowly conceived closed-door affair.

Los Angeles Times

Expert writing Tips

Best practice

When presenting "honest data", always cite your sources to enhance credibility and allow for verification of the information.

Common error

Be cautious when interpreting "honest data"; avoid jumping to conclusions about cause-and-effect relationships without further analysis and evidence.

Linguistic Context

The phrase "honest data" functions as an adjective-noun compound, where the adjective "honest" modifies the noun "data". This indicates that the data is truthful, accurate, and free from deceit or manipulation. As Ludwig AI underlines, this phrase is correct and usable in written English.

Expression frequency: Uncommon

Frequent in

News & Media

30%

Science

40%

Formal & Business

30%

Less common in

Wiki

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "honest data" is a grammatically sound phrase used to describe information that is accurate, truthful, and reliable. As Ludwig AI confirms, it is correct and usable in written English. While its frequency is uncommon, it appears across various contexts, including news, science, and business. Ensuring data integrity and transparency is crucial, and using the term "honest data" emphasizes this importance. When presenting data, remember to cite sources and avoid drawing unsubstantiated causal conclusions. In situations where "honest data" seems redundant, consider alternatives such as "accurate information" or "reliable statistics".

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

accurate information

Focuses on the correctness and precision of the information.

reliable statistics

Emphasizes the dependability and trustworthiness of the numerical data.

verifiable facts

Highlights the ability to confirm the truthfulness of the data.

truthful findings

Stresses the honesty and absence of deception in the reported results.

unbiased data

Indicates the data is free from prejudice or favoritism.

genuine results

Implies authenticity and a lack of manipulation in the findings.

authentic data

Highlights that the data is original and not fabricated.

valid data

Focuses on whether the data measures what it is supposed to measure.

credible data

Highlights the trustworthiness and believability of the data.

transparent information

Emphasizes the openness and clarity with which the information is presented.

FAQs

How can I ensure that I am working with "honest data"?

To ensure you're using "honest data", verify its source, check for biases, and cross-validate it with other reliable sources. Always consider the methodology used to collect the data.

What does it mean for data to be considered "honest"?

For data to be considered "honest", it must be accurate, unbiased, and free from manipulation or fabrication. It should reflect the true state of affairs as objectively as possible.

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

You can use alternatives like "accurate information", "reliable statistics", or "verifiable facts" to convey a similar meaning depending on the context.

Why is "honest data" important in research and decision-making?

"Honest data" is crucial because it forms the foundation for sound research and informed decisions. Using flawed or dishonest data can lead to incorrect conclusions and detrimental outcomes.