validating data

USAGE SUMMARY

The phrase "validating data" is correct and usable in written English.
It is typically used in contexts related to data processing, analysis, or software development, where it refers to the process of checking the accuracy and quality of data. Example: "Before we proceed with the analysis, we need to ensure that we are validating data to avoid any errors in our results."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

35 human-written examples

At this point the model error for the validating data suddenly increases.

Applied Water Science

Figure 8 illustrates the validating data results of our proposed predictor.

EURASIP Journal on Wireless Communications and Networking

LVI tests with different impact energies were conducted to provide validating data.

Composite Structures

The content can be used to enable automated QC, such as selection of appropriate precision and accuracy, as well as validating data using specified operational ranges.

Open Geospatial Data, Software and Standards

In general, the error for the validating data set tends to decrease as the training takes place up to the point that overfitting begins.

Applied Water Science

Think about a hierarchy of validating data, with that kind of thin stuff on the extreme low end, and paying customers / off-the-charts usage at the high end.

TechCrunch

Human-verified similar examples from authoritative sources

Similar Expressions

25 human-written examples

A database design-support environment supports a data analyst in eliciting, articulating, specifying and validating data-related requirements.

Information Systems

The disagreement is really over the value of intuition: the E.B.M. position is that there are reliable, validated data, and then there are data that aren't reliable and validated, and that's really what matters.

The New York Times - Magazine

A white paper on reform of medical negligence was promised for early 2002 and the creation of an independent office for information on healthcare performance, to collect and validate data on clinical performance, was announced.

The Guardian

Quality models are supposed to apply validated data sets.

Chemistry Central Journal

Gathering data can be challenging, but the Gravity Earth platform allows third parties to provide and validate data on users.

TechCrunch

Expert writing Tips

Best practice

When working with large datasets, implement automated processes for "validating data" to improve efficiency and reduce human error.

Common error

Don't solely focus on typical scenarios when "validating data". Ensure your validation covers all possible edge cases and exceptional inputs to prevent unexpected errors.

Linguistic Context

The phrase "validating data" functions as a gerund phrase, often serving as the subject or part of the subject in a sentence. It describes the action of checking the quality and accuracy of data, as shown by Ludwig AI in several examples.

Expression frequency: Common

Frequent in

Science

76%

News & Media

12%

Formal & Business

12%

Less common in

Wiki

0%

Academia

0%

Encyclopedias

0%

Ludwig's WRAP-UP

In summary, "validating data" is a grammatically correct and commonly used gerund phrase that emphasizes the importance of ensuring the accuracy and reliability of data. Ludwig AI indicates that it is primarily used in scientific, technical, and professional contexts. Key alternatives include "verifying data" and "confirming data integrity". When using this phrase, remember to implement automated processes for large datasets and consider all possible edge cases to prevent errors. By understanding the nuances and best practices associated with "validating data", writers can effectively communicate the importance of data quality in their respective fields.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

verifying data

Focuses on confirming the correctness of data through a specific process or check.

confirming data integrity

Emphasizes ensuring data remains complete and unaltered during any operation.

authenticating data

Highlights proving the data's origin and ensuring it has not been tampered with.

assessing data quality

Shifts focus to evaluating the data's suitability for its intended purpose.

checking data accuracy

Simplifies the concept to a basic review of the data's correctness.

corroborating data

Involves confirming data through additional sources or evidence.

substantiating data

Implies providing evidence to support the validity of the data.

verifying data sets

Specifically refers to the act of checking collections of related data for errors.

evaluating data reliability

Focuses on determining the trustworthiness and consistency of the data.

quality control of data

Implies a systematic process for ensuring the data meets specific standards.

FAQs

How is "validating data" used in research?

In research, "validating data" is crucial for ensuring the reliability and accuracy of findings. It involves verifying data collection methods, checking for errors, and comparing results with other data sources to confirm their validity.

What are some techniques for "validating data"?

Common techniques for "validating data" include cross-validation, which uses separate data sets to verify the results; statistical tests to check for significant deviations; and manual review to identify anomalies and inconsistencies.

What's the difference between "verifying data" and "validating data"?

"Verifying data" typically refers to confirming that data matches a known standard or specification, while "validating data" is a broader term that encompasses ensuring the data is accurate, relevant, and useful for its intended purpose. Validation often includes verification but goes further in assessing the overall quality.

What are the consequences of not "validating data"?

Failing to "validating data" can lead to inaccurate results, flawed conclusions, and poor decision-making. Inaccurate data can compromise the integrity of research, lead to financial losses, and damage reputations.