missing data points

USAGE SUMMARY

"missing data points" is correct and usable in written English.
You can use it when referring to information that is missing from a set of data or a data analysis. For example, "We identified several missing data points that needed to be filled in before we could complete the analysis."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

b716 missing data points.

Injury Epidemiology

cListwise deletion for missing data on vehicle occupants and race rendered 1,343 missing data points.

Injury Epidemiology

There are also missing data points for a number of hours on other days.

International Journal of Energy and Environmental Engineering

Fortunately, these missing data points did not seriously affect this study.

EURASIP Journal on Advances in Signal Processing

Missing data points are due to not enough total GPU memory.

Advanced Structural and Chemical Imaging

Missing data points are due to not enough total GPU memory Fig. 6 Cone-beam performance for different GPU counts.

Advanced Structural and Chemical Imaging

Missing data points are due to not enough total GPU memory Fig. 7 Parallel-beam performance for different volume sizes.

Advanced Structural and Chemical Imaging

A particle filter was used to track, the choice motivated given missing data points due to occlusion.

EURASIP Journal on Image and Video Processing

Missing data points are due to not enough total GPU memory Fig. 9 Effects of distribution of GPUs over nodes.

Advanced Structural and Chemical Imaging

This method was shown to work well with short intervals of missing data points (Junninen et al. 2004).

Environmental Systems Research

Additional time points are introduced between the samples [24], and the corresponding values of are treated as missing data points.

EURASIP Journal on Advances in Signal Processing

Expert writing Tips

Best practice

Clearly explain the method used to handle "missing data points", such as imputation or exclusion.

Common error

Failing to address the potential bias introduced by "missing data points" can lead to inaccurate or misleading results. Always acknowledge and discuss the limitations.

Linguistic Context

The phrase "missing data points" functions as a noun phrase, typically serving as the subject or object of a sentence. It identifies a specific type of data imperfection. As Ludwig AI explains, this is a correct and usable phrase.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "missing data points" is a grammatically sound and commonly used term, especially within scientific and research contexts. Ludwig AI confirms its correct usage. The phrase refers to the absence of specific values within a dataset, a situation that necessitates careful consideration in data analysis. Best practices involve clearly reporting the extent of missing data and the methods used to address it, while avoiding the common error of ignoring its potential implications. Related phrases, such as "absent data values" or "incomplete data records", offer alternative ways to express the same concept. The formal and scientific register reflects its technical application, predominantly found in academic papers and research reports. The examples provided by Ludwig showcase the importance of understanding and managing "missing data points" to ensure the integrity of data analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

absent data values

Replaces "points" with "values", focusing on the numerical or qualitative aspect of the missing information.

incomplete data records

Shifts focus to the entire record being incomplete due to missing elements.

unavailable data entries

Emphasizes the lack of access to specific data entries.

gaps in the data

Uses a metaphorical term ("gaps") to describe the missing information.

data voids

A more concise and technical term for missing information.

data omissions

Focuses on the act of leaving out data.

lacking data observations

Highlights the absence of observed data.

deficient data segments

Emphasizes that portions of the data are lacking.

unfilled data fields

Focuses on the fields within a dataset that lack data.

blanks in the dataset

A more informal way to refer to missing data.

FAQs

How should I handle "missing data points" in my analysis?

Common methods include imputation (replacing missing values with estimated values) or excluding records with missing data. The choice depends on the amount and pattern of missing data, as well as the analysis being performed.

What are some alternatives to saying "missing data points"?

You can use alternatives like "absent data values", "incomplete data records", or "unavailable data entries" depending on the context.

Is it always necessary to impute "missing data points"?

No, imputation is not always necessary or appropriate. If the amount of missing data is small and randomly distributed, and the analysis is robust, it may be acceptable to proceed without imputation. However, it is crucial to assess the potential impact of missing data on the results.

How do "missing data points" affect statistical analysis?

"Missing data points" can reduce statistical power, introduce bias, and complicate the interpretation of results. Addressing missing data appropriately is crucial for ensuring the validity and reliability of statistical analyses.