irregularities of data

USAGE SUMMARY

The phrase "irregularities of data" is correct and usable in written English.
It can be used when discussing inconsistencies, anomalies, or errors found within a dataset. Example: "The analysis revealed several irregularities of data that need to be addressed before proceeding with the report."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

The study had some limitations because of irregularities of data in the delivery register.

BMC Pregnancy and Childbirth

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

The existing algorithms are not applicable in practical problems due to the irregularity of data.

International Journal of Disaster Risk Science

They found that sleep and appetite/weight is the pair of less correlated factors and sleep variability is inversely correlated with irregularity of data.

International Journal of Bipolar Disorders

These needs are: Positioning irregularities of measured data made it difficult to determine where maintenance activities were to be conducted.

Journal of Modern Transportation

Positioning irregularities of measured data made it difficult to determine where maintenance activities were to be conducted.

Journal of Modern Transportation

Bearing in mind the spatial irregularity of Intermagnet data, it is strongly desirable to analyze the signals captured during time spans when the source has a spatial structure as simple as possible.

Earth, Planets and Space

In practice, limiting the irregularity of CGM data can be extremely useful to reduce the number of false hypo- and hyperglycemic alerts generated by the CGM system, with obvious benefits for the diabetic patient.

Diabetes Care

Given the aforementioned irregularities of a semistructured data, a proximon object is essentially a multidimensional list where dimensionality is constant but the length and contents of a list at any given dimension are highly variable (see Figure 2).

Database : the journal of biological databases and curation

XGBoost (eXtreme Gradient Boosting) library [78] is based on boosted trees by Gradient Boosting Machine (GBM) [79] and is a highly sophisticated algorithm robust against all kind of data irregularities.

EURASIP Journal on Advances in Signal Processing

We then excluded 31 individuals because of data irregularities.

Diabetes Care

After removal of data irregularities, we had information on 29,148 individuals with CD [ 33].

BMC Gastroenterology

Expert writing Tips

Best practice

Always cross-validate your data sources to minimize the "irregularities of data". Doing so will assure that your findings are valid and meaningful.

Common error

Avoid using "irregularities of data" as a catch-all term. Clearly define the specific types of irregularities you are addressing, such as missing values, outliers, or inconsistencies, to improve clarity and precision.

Linguistic Context

The phrase "irregularities of data" functions as a noun phrase, typically serving as the subject or object of a sentence. As Ludwig AI confirms, it’s used to denote problems within a dataset. Examples from Ludwig illustrate its usage in scientific and news contexts.

Expression frequency: Common

Frequent in

Science

65%

News & Media

20%

Formal & Business

15%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "irregularities of data" is a noun phrase used to describe inconsistencies, anomalies, or errors within a dataset. According to Ludwig AI, the phrase is grammatically correct and suitable for use in written English, particularly in science, news, and business contexts. Common alternatives include "data anomalies", "data inconsistencies", and "data errors". When using this phrase, it's crucial to be specific about the types and sources of irregularities to ensure clarity and accuracy. Therefore, users should avoid using "irregularities of data" as an all-encompassing term, but specify the issue with specific terms such as "outliers" or "missing values".

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data anomalies

Focuses specifically on deviations from the expected or normal values in the data.

data inconsistencies

Highlights the lack of uniformity or coherence within the data.

data errors

Emphasizes mistakes or inaccuracies present in the data.

data discrepancies

Similar to inconsistencies, but often used when comparing different sets of data.

data aberrations

Implies a significant deviation from the norm, often suggesting a problem.

data abnormalities

Similar to anomalies, but may suggest a more severe or unexpected deviation.

inaccurate data

Directly states that the data contains factual errors.

faulty data

Indicates that the data is flawed or defective in some way.

corrupted data

Suggests that the data has been damaged or compromised, leading to errors.

non-standard data

Implies the data doesn't conform to expected norms or formats.

FAQs

What does "irregularities of data" mean?

The phrase "irregularities of data" refers to inconsistencies, anomalies, errors, or other issues that compromise the quality and reliability of a dataset. These irregularities can affect the validity of analyses and conclusions drawn from the data.

What are some common examples of "irregularities of data"?

Common examples include missing values, duplicate entries, outliers, inconsistent formatting, and inaccurate measurements. These issues can arise from various sources, such as human error, faulty equipment, or data processing errors.

How can I identify "irregularities of data" in my dataset?

You can identify "irregularities of data" through various methods such as data profiling, statistical analysis, and visualization techniques. Data profiling helps summarize the characteristics of the data, while statistical analysis can detect outliers and inconsistencies. Visualization tools can help reveal patterns and anomalies that might not be apparent otherwise.

What can I say instead of "irregularities of data"?

You can use alternatives like "data anomalies", "data inconsistencies", or "data errors" depending on the specific context.