inconsistency check

USAGE SUMMARY

The phrase "inconsistency check" is correct and usable in written English.
It is typically used in technical or analytical contexts to refer to a process that verifies whether data or results are consistent and reliable. Example: "Before finalizing the report, we need to perform an inconsistency check to ensure all data aligns correctly."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

It replaces the inconsistency check in the Chi2 with a level of consistency that is approximated after each step of discretization.

Human-centric Computing and Information Sciences

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

We have defined three possible patterns of inconsistent response (see table 4 Inconsistency checking).

BMJ Open

Then, the inconsistency checking would be made using the attributes of p in a's CV and in the digital library.

Journal of the Brazilian Computer Society

In this paper, we provide such an analysis and provide algorithms for checking and removing inconsistency, checking for rule enforceability, and minimally updating the rules to ensure enforceability whenever possible using only the existing parties.

Computers & Security

Quality-control tests for genotyping calls included the following measures: samples were required to have an overall genotype call efficiency of ≥0.98; Mendelian inconsistency checks were performed using WASP [23]; reported gender and genetically determined gender were examined with the use of X-linked SNPs, and we required a conservative gencall score of 0.25.

Plosone

Distribution and summary statistics were examined for all variables and inconsistency checks were also performed.

BMC Geriatrics

Errors and inconsistency checks are provided to ensure a robust model.

Bioinformatics

The software was customised and allowed for inconsistency checks of the data during entry.

Global Health Action

26 Inconsistency checks were performed for closed loops in the network.

British Medical Journal

Weekly data were downloaded in the field to programmed netbooks, from which data inconsistency checks were undertaken.

American Journal of Tropical Medicine and Hygiene

Data entry used built-in error and logic inconsistency checking (Epi Info Version 6, Centers for Disease Control and Prevention, Atlanta, GA).

BMC Oral Health

Expert writing Tips

Best practice

When performing a "inconsistency check", clearly define the criteria for what constitutes an inconsistency to ensure consistent and reliable results.

Common error

Avoid using "inconsistency check" as a blanket term for all data validation processes. It specifically refers to identifying conflicting or contradictory information, not necessarily all types of errors.

Linguistic Context

The phrase "inconsistency check" functions as a noun phrase, typically serving as a direct object or a subject complement. Ludwig AI confirms its role in referring to a process for verifying data reliability.

Expression frequency: Common

Frequent in

Science

95%

Formal & Business

3%

News & Media

2%

Less common in

Academia

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "inconsistency check" is a noun phrase used to describe the process of identifying contradictions or logical errors in data. According to Ludwig AI, the phrase is grammatically correct and usable in written English, particularly in technical or analytical contexts. It's most commonly found in scientific and formal settings. While alternatives like "consistency verification" or "data validation check" exist, "inconsistency check" remains a clear and direct way to refer to this specific verification process. Performing such checks are vital for ensuring accuracy in data-driven fields.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

consistency verification

Focuses specifically on verifying whether the data is consistent rather than just identifying inconsistencies.

data validation check

Emphasizes the act of validating the data, making it suitable when data accuracy is the main concern.

error detection

Broader term referring to the process of finding errors, not necessarily inconsistencies.

validation process

Refers to the overall procedure of validating data, which might include other steps besides checking for inconsistencies.

integrity check

Highlights the verification of data integrity, which includes consistency but can also encompass other aspects.

flaw identification

Focuses on finding flaws or defects, which may or may not be related to inconsistencies.

verification procedure

General term for a process of verifying data, with less emphasis on the specific type of check.

quality assurance

Describes a comprehensive process to maintain data quality, where inconsistency checks are one of many potential steps.

accuracy confirmation

Highlights the importance of confirming the accuracy of the data or results.

cross-validation

Specifically involves comparing different sets of data to validate findings.

FAQs

How is an "inconsistency check" performed?

An "inconsistency check" involves comparing different pieces of information to identify contradictions or logical errors. This can be done manually or through automated tools, depending on the volume and complexity of the data.

What is the purpose of an "inconsistency check"?

The purpose of an "inconsistency check" is to ensure data accuracy and reliability. By identifying and resolving inconsistencies, you can improve the quality of your data and the validity of any analyses or decisions based on it.

What are some alternatives to "inconsistency check"?

You can use alternatives like "consistency verification", "data validation check", or "error detection" depending on the specific context.

Why is it important to perform an "inconsistency check"?

Performing an "inconsistency check" is crucial for maintaining the integrity of data. Failing to do so can lead to flawed conclusions, incorrect decisions, and potentially serious consequences in fields like science, finance, and healthcare.