data is sparse

USAGE SUMMARY

The phrase "data is sparse" is correct and usable in written English.
It can be used when describing a situation where there is a lack of sufficient data or information available for analysis or decision-making. Example: "In this study, we found that the data is sparse, making it difficult to draw definitive conclusions."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

Nevertheless, the data is sparse.

MIT Technology Review

But conclusive medical data is sparse.

The New York Times

But in such a remote region, actual data is sparse.

The Guardian

– Estimation, Optimization, and Parallelism when Data is Sparse by John Duchi, Mike Jordan and Brendan McMahan.

Princeton University

Because grounding accidents are rare events and data is sparse, their analysis requires a probabilistic approach.

Marine Structures

End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse.

Knowledge-Based Systems

While today's computing units excel at processing dense and regular data, their performance is questionable when the data is sparse.

Journal of Parallel and Distributed Computing

Intersection-based measures do not accurately capture similarity in certain domains, such as when the data is sparse or when there are known relationships between items within sets.

Stanford University

"How do you decide who lives and who dies? Are you looking for people who are more educated, or wealthier, or those who have a particular sexual preference?" The data is sparse.

The New Yorker

Conversely, findings that have some substantive, real-world impact may not be deemed statistically significant, if the data is sparse or noisy.

The New York Times

Because data is sparse in this region, David Titley, a professor of meteorology at Penn State and Arctic climate expert, suggested "a little" caution in interpreting the chart but said he considers it "basically right" given other data.

The Washington Post

Expert writing Tips

Best practice

When stating that "data is sparse", clearly explain the implications of this sparsity. For instance, mention how it might affect the reliability of conclusions or the need for further investigation.

Common error

Avoid making definitive claims or generalizations when the "data is sparse". Instead, acknowledge the limitations and suggest areas for further research or data collection.

Linguistic Context

The phrase "data is sparse" functions as a descriptive statement, indicating that the available data is limited or insufficient. As Ludwig AI confirms, this phrase is grammatically correct and conveys a clear meaning. It is often used to qualify findings or explain limitations in research and analysis.

Expression frequency: Very common

Frequent in

Science

52%

News & Media

33%

Academia

15%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "data is sparse" is a common and grammatically sound way to express that available information is limited. As Ludwig AI indicates, this phrase is appropriate for use in written English. Its frequency is considered "very common", particularly in scientific, news, and academic contexts. Related phrases include "data is limited" and "data is scarce". When using this phrase, it's important to acknowledge potential limitations in analysis and avoid overstating conclusions. Awareness of its implications and related terms enhances clarity and precision in communication.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Data is limited

Replaces "sparse" with "limited", focusing on the restricted amount of data.

Data is scarce

Uses "scarce" as a direct synonym for "sparse", highlighting its rarity.

Data is insufficient

Emphasizes that the data is not enough for a specific purpose.

Data is incomplete

Focuses on the lack of wholeness or full representation in the data.

Data is lacking

Highlights the absence of necessary data points.

Data is fragmented

Indicates that the data exists in disconnected pieces.

Data is spotty

Suggests that the data is inconsistently available or reliable.

Data is thin

Implies a lack of depth or substance in the available data.

Data availability is limited

Expands the phrase to explicitly mention the restriction in data availability.

Data coverage is poor

Focuses on the inadequate representation of the subject matter by the data.

FAQs

What does it mean when someone says "data is sparse"?

Saying that "data is sparse" means there isn't enough information available. This can make it difficult to draw reliable conclusions or make informed decisions. It highlights a limitation in the available evidence.

How does data sparsity affect machine learning models?

When "data is sparse", machine learning models may not generalize well to new, unseen data. This can lead to overfitting, where the model learns the training data too well but performs poorly on real-world data. Gathering more data or using techniques like data augmentation can help mitigate this issue.

What are some alternatives to saying "data is sparse"?

You can use alternatives like "data is limited", "data is insufficient", or "data is scarce" depending on the context. These phrases all convey a similar meaning of insufficient or incomplete data.

How do researchers deal with sparse data?

Researchers address sparse data through techniques like data imputation (filling in missing values), data augmentation (creating synthetic data), using models robust to sparsity, or acknowledging limitations and focusing on descriptive analysis. They also prioritize collecting more comprehensive data in future studies.