missing data

USAGE SUMMARY

"missing data" is correct and usable in written English.
It can be used when referring to a situation that involves incomplete or inadequate data. For example: "Due to missing data, it was difficult to draw meaningful conclusions from the survey."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Blanks indicate missing data.

The New York Times

NOTES -- Blanks indicate missing data.

The New York Times

The missing data wasn't the first lapse.

The New York Times

There were very few missing data.

Nature

Rubin, D. B. Inference and missing data.

Nature

How to deal with missing data.

Nature

where ' ∗' denotes missing data.

Complex Adaptive Systems Modeling

b716 missing data points.

Injury Epidemiology

missing data technique.

EURASIP Journal on Audio, Speech, and Music Processing

NA: missing data.

Large-scale Assessments in Education

The missing data is short-time missing data.

EURASIP Journal on Wireless Communications and Networking

Expert writing Tips

Best practice

When reporting research results, clearly state how "missing data" were handled, such as through imputation or exclusion, to ensure transparency and reproducibility.

Common error

Avoid assuming that "missing data" are randomly distributed without proper analysis. Non-random "missing data" can lead to biased results and inaccurate conclusions.

Linguistic Context

The phrase "missing data" functions as a noun phrase, typically acting as the subject or object of a sentence. It refers to data values or pieces of information that are not available or recorded. Ludwig AI confirms its usability across diverse contexts.

Expression frequency: Very common

Frequent in

Science

65%

News & Media

18%

Academia

10%

Less common in

Formal & Business

4%

Wiki

1%

Encyclopedias

1%

Ludwig's WRAP-UP

In summary, the phrase "missing data" is a common and grammatically correct term used to describe the absence of information in a dataset. As confirmed by Ludwig, it is most prevalent in scientific, academic, and news-related contexts. When using this phrase, it's important to be aware of the implications of "missing data" on analysis and results, and to clearly communicate how it was addressed in any research or reporting. Be mindful of not assuming that missing values are random as this may create bias. Consider using alternatives like "incomplete datasets" or "data gaps" to add nuance to your writing. Overall, a thorough understanding of "missing data" and its handling is crucial for conducting rigorous and transparent research.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

incomplete datasets

Refers specifically to datasets that have some fields or entries lacking data.

data gaps

Highlights the presence of gaps or lacunae within a collection of data.

absent information

Emphasizes the lack of information, which may be broader than just numerical data.

data unavailability

Focuses on the state where data cannot be accessed or is not present.

lack of data

A more general term indicating a deficiency in the amount of data available.

data deficiency

Highlights the inadequacy or incompleteness of available data.

data omission

Implies that data has been intentionally or unintentionally left out.

data imputation

Refers to the process of filling in "missing data" with estimated values.

data scarcity

Indicates a limited or insufficient quantity of data.

gaps in knowledge

Broader term indicating a deficiency of knowledge of a specific topic, that can be also derived from a lack of data.

FAQs

How to use "missing data" in a sentence?

You can use "missing data" to describe situations where information is incomplete or absent, such as "The analysis was hampered by the amount of missing data" or "The report acknowledges the limitations caused by missing data".

What are some ways to deal with "missing data" in a research study?

Researchers commonly use techniques like imputation, where missing values are estimated based on other available data, or they might exclude records with "missing data" from the analysis. The choice depends on the nature and amount of missingness.

What does "data imputation" mean?

"Data imputation" is a technique used to replace "missing data" with estimated values. This can help to avoid bias and improve the accuracy of statistical analyses. Common methods include mean imputation, regression imputation, and multiple imputation.

What's the difference between "data gaps" and "missing data"?

While both terms refer to incomplete information, "data gaps" often imply a broader lack of information across a dataset or field, whereas "missing data" usually refers to specific data points absent within a record or observation. They can often be used interchangeably, but "data gaps" might imply a more systemic problem.