incompleteness of data

USAGE SUMMARY

The phrase "incompleteness of data" is correct and usable in written English.
It can be used when discussing issues related to insufficient or lacking information in a dataset or research context. Example: "The incompleteness of data in the study led to inconclusive results and raised questions about the validity of the findings."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

43 human-written examples

Measuring errors, mathematical errors and incompleteness of data are the main sources of uncertainty in reservoir modeling.

Experimental Design in Petroleum Reservoir Studies

The combination of two sets will deal with all aspects of vagueness, inconsistency and incompleteness of data and information, and then will enhance the quality of introduced services and decisions from smart cities to their citizens.

Measurement

The artifacts were presumably caused by breathing irregularity and incompleteness of data acquisition (95% acquired only).

Duke University

The Airline suggested that this might be due to incompleteness of data.

Journal of Big Data

Conversely, incompleteness of data, such as lack of the offshore data, may lead to difficulty in inferring the individual elastic effects.

Earth, Planets and Space

The method has 93% average accuracy of prediction (leave-one-out cross-validation – LOO CV) and is robust to the incompleteness of data (http://www.ibmc.msk.ru/pass).ru/pass

Chemistry Central Journal

Human-verified similar examples from authoritative sources

Similar Expressions

17 human-written examples

Inadequate sequence generation Inadequate allocation concealment Lack of blinding of participants, providers, data collectors, outcome adjudicators and data analysts Incompleteness of outcome data Selective outcome reporting, and other bias.

BMJ Open

According to recommendations outlined in the Cochrane Handbook, we used the following criteria for assessing the risk of bias in randomised studies: Inadequate sequence generation Inadequate allocation concealment Lack of blinding of participants, providers, data collectors, outcome adjudicators and data analysts Incompleteness of outcome data Selective outcome reporting, and other bias.

BMJ Open

According to recommendations outlined in the Cochrane Handbook, we used the following criteria for assessing the risk of bias in randomized studies: Inadequate sequence generation; Inadequate allocation concealment; Lack of blinding of participants, providers, data collectors, outcome adjudicators, and data analysts Incompleteness of outcome data; Selective outcome reporting, and other bias.

BMC Nursing

The main reason behind the non-utility of the clickstream data remains is the incompleteness of the data, and the huge size of the logged data.

Journal of Internet Services and Applications

The HRH data may be biased due to incompleteness of the data collection process.

Human Resources for Health

Expert writing Tips

Best practice

When discussing research limitations, clearly state how the "incompleteness of data" affects the study's conclusions and potential biases.

Common error

Avoid exaggerating the effect of "incompleteness of data" without providing specific examples or evidence. Quantify the missing data and its likely influence on results.

Linguistic Context

The phrase "incompleteness of data" functions as a noun phrase, typically serving as the subject or object of a sentence. As Ludwig AI confirms, it's grammatically correct and widely used.

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 "incompleteness of data" is a grammatically correct and frequently used term, especially in scientific and academic fields, to describe limitations in available information. Ludwig AI confirms its usability and relevance. Addressing this issue requires careful consideration and mitigation strategies to reduce potential biases. Alternative phrases like "lack of data" or "data deficiency" can be used to express similar concepts. When writing about this topic, it's essential to clearly state the impact of data limitations on results.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Data deficiency

Focuses on the lack of necessary information.

Lack of data

Directly states the absence of sufficient data.

Insufficient data

Emphasizes that the data is not adequate.

Missing data

Highlights the absence of specific data points.

Gaps in data

Indicates specific areas where data is lacking.

Partial data

Suggests that only a portion of the data is available.

Limited data

Implies that the amount of data is restricted.

Fragmentary data

Suggests that the data is broken into incomplete pieces.

Inadequate data

Similar to insufficient data, but may imply a stronger deficiency.

Deficient data

Emphasizes the lack of necessary or required information.

FAQs

How does the "incompleteness of data" affect research outcomes?

The "incompleteness of data" can lead to biased results, limit the generalizability of findings, and reduce the statistical power of analyses. Addressing this limitation requires careful consideration during the study design and interpretation phases.

What are some alternatives to "incompleteness of data"?

You can use alternatives like "lack of data", "data deficiency", or "insufficient data" to convey a similar meaning.

How can I mitigate the issues caused by the "incompleteness of data"?

Employ statistical methods to handle missing data, such as imputation techniques, sensitivity analyses to assess the robustness of results, and acknowledge the limitations in the study's conclusion.

Is it always necessary to explicitly mention the "incompleteness of data" in a report?

Yes, it is important to acknowledge the "incompleteness of data", particularly in research reports, as it demonstrates transparency and allows readers to assess the potential impact on the findings and conclusions. Omitting this information can compromise the credibility of the study.