data distribution

USAGE SUMMARY

The term "data distribution" is a valid phrase in English, and it can be used in both spoken and written English.
For example: "Our team collected data on customer preference, and through our analysis we determined the data distribution of their interests."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

For web data, distribution costs would be close to zero.

The Guardian - Tech

Still, a freeze on all data distribution is likely to be in effect for the foreseeable future.

The New York Times - Tech

Fig. 3 Data distribution.

EURASIP Journal on Image and Video Processing

b The data distribution.

EURASIP Journal on Wireless Communications and Networking

Fig. 1 DrugBank data distribution.

Journal of Cheminformatics

Data distribution of different NR targets.

Journal of Cheminformatics

Effect of data distribution In Fig. 8, we study the effect of data distribution on algorithms.

Data Science and Engineering

This process is assumed to transform the test data distribution into the training data distribution.

EURASIP Journal on Audio, Speech, and Music Processing

The kernel density estimation method analyzes the data distribution without using the prior knowledge of data distribution and without making any assumptions to data distribution.

Chemistry Central Journal

Th data distribution is usually done using broadcast method.

Vehicular Communications

From experimental data, distribution coefficients of methanol have been determined.

Fluid Phase Equilibria

Expert writing Tips

Best practice

Use visualizations like histograms and box plots to effectively illustrate "data distribution" patterns in reports and presentations.

Common error

Avoid using "data distribution" when you actually mean data collection. "Data distribution" refers to how data is spread or arranged, while data collection is the process of gathering data.

Linguistic Context

The phrase "data distribution" primarily functions as a noun phrase. It refers to the arrangement, spread, or allocation of data values within a dataset or system. Ludwig's examples showcase its use in various scientific and technical contexts.

Expression frequency: Very common

Frequent in

Science

85%

News & Media

10%

Formal & Business

5%

Less common in

Wiki

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

In summary, the phrase "data distribution" is a grammatically sound and frequently used term, primarily in scientific and technical fields. Ludwig AI confirms its validity and provides numerous examples. The phrase refers to the arrangement or spread of data, often analyzed using statistical methods. It is crucial to differentiate it from terms like data collection, and to specify the type of distribution when possible for clarity. Related concepts include "distribution of data", "data dissemination", and "data spread". Understanding "data distribution" is essential for accurate data analysis and modeling.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

distribution of data

Reorders the phrase to emphasize the "distribution" aspect, functioning as a noun phrase.

data dissemination

Focuses on the act of spreading or sharing data, rather than the arrangement.

data spread

A more concise way of expressing how data is scattered or dispersed.

data arrangement

Highlights the structured organization of data elements.

data allocation

Emphasizes how data resources are assigned or distributed.

data dispersion

Focuses on the scattering of data points in a dataset.

data delivery

Highlights the act of transporting data to its intended recipients.

data transmission

Focuses on the process of sending data from one point to another.

data reporting

Shifts the focus to the presentation of data in a structured format.

data availability

Emphasizes the accessibility and provision of data.

FAQs

How is "data distribution" typically assessed?

"Data distribution" is commonly assessed using statistical tests such as the Kolmogorov-Smirnov test, Shapiro-Wilk test, and visual inspection of histograms and Q-Q plots. These methods help determine if the data follows a normal or other known distribution.

Why is understanding "data distribution" important?

Understanding "data distribution" is crucial for selecting appropriate statistical tests, building accurate models, and making valid inferences from data. Different statistical methods assume different distributions, so choosing the right approach depends on the underlying "data distribution".

What are common types of "data distribution"?

Common types of "data distribution" include normal (Gaussian), uniform, exponential, Poisson, binomial, and skewed distributions. The specific type of distribution depends on the nature of the data and the process that generated it.

What is the difference between "data distribution" and "data dispersion"?

"Data distribution" refers to the overall shape and pattern of data values, while "data dispersion" focuses on the spread or variability of the data. Dispersion measures like variance and standard deviation quantify how much the data points deviate from the mean, complementing the information provided by the distribution's shape.