necessary data for

USAGE SUMMARY

The phrase "necessary data for" is correct and can be used in written English.
You can use it to refer to information or pieces of evidence that are required to reach a particular conclusion. For example, "We collected the necessary data for our research project."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The necessary data for evaluation are compiled from existing information.

Forest Ecology and Management

Onfido: Not backed by Passion, it automates the collection of necessary data for firms needing background checks on individuals.

The Guardian - Business

Our aims were to supply necessary data for the future design of inertial fusion reactor chambers.

Journal of Nuclear Materials

(B) Explanation of the processes and methods used to collect the necessary data for the GHG calculations.

Cornell University

Indeed, the ionic concentrations of the pore solution are necessary data for the application of the Nernst-Planck equation.

Construction and Building Materials

Monte-Carlo simulations are used for generating feature vector samples and outputs for construction of necessary data for ANFIS.

Applied Mathematical Modelling

The present research supported necessary data for the green extraction method of edible oil in food industry.

Food Chemistry

A statistical sampling design is proposed to generate necessary data for the traffic emission inventory in a city.

Atmospheric Research

The simulation results provide necessary data for the reactor design and the process scale-up of the FTS.

Chemical Engineering and Processing: Process Intensification

Its primary use is to demonstrate achievable effluent quality and collect necessary data for economic and process scale-up modelling.

Membrane Technology

A full factorial design of experiment (DOE) is used to collect the necessary data for developing and validating the models.

Measurement

Expert writing Tips

Best practice

When writing scientific reports or business analyses, ensure that all claims are supported by the "necessary data for" drawing valid conclusions. Always cite your data sources to maintain transparency and credibility.

Common error

Avoid assuming causation based solely on correlation within your "necessary data for". Always perform rigorous statistical tests to establish a causal relationship.

Linguistic Context

The phrase "necessary data for" functions as a noun phrase acting as a determiner. It indicates the essential information needed to perform a task, make a decision, or reach a conclusion. As Ludwig AI explains, it is grammatically correct and can be used to emphasize the information that is required.

Expression frequency: Very common

Frequent in

Science

78%

News & Media

10%

Formal & Business

5%

Less common in

Encyclopedias

2%

Academia

2%

Wiki

1%

Ludwig's WRAP-UP

In summary, "necessary data for" is a grammatically sound and frequently used phrase, predominantly found in scientific and formal contexts. As Ludwig AI confirms, it serves to highlight the indispensable information required for various processes. When using this phrase, ensure that you are referring to data that is truly essential for the task at hand and avoid common pitfalls like confusing correlation with causation. By following best practices and being mindful of common errors, you can effectively use "necessary data for" to strengthen your writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

required information for

Replaces "necessary data" with "required information", focusing on the indispensable aspect of the data.

essential facts for

Substitutes "data" with "facts" and "necessary" with "essential", emphasizing the fundamental importance of the information.

indispensable figures for

Employs "indispensable" instead of "necessary" and "figures" as a variation of "data", highlighting the crucial nature of the numerical information.

vital statistics for

Replaces "data" with "statistics" and "necessary" with "vital", suitable when referring to critical numerical information.

requisite knowledge for

Uses "requisite knowledge" in place of "necessary data", applicable when the data represents a body of understanding.

crucial evidence for

Replaces "necessary data" with "crucial evidence", which is fitting when the data serves as key proof or support.

fundamental inputs for

Changes "necessary data" to "fundamental inputs", appropriate in contexts where the data is used as a primary element in a process or system.

key metrics for

Swaps "necessary data" for "key metrics", suitable for contexts involving performance measurement and assessment.

relevant statistics for

Replaces "necessary data" with "relevant statistics", highlighting that only data relevant to the particular context is considered.

pertinent information for

Utilizes "pertinent information" instead of "necessary data", stressing the relevance and applicability of the information.

FAQs

How can I effectively gather the "necessary data for" my research?

Start by defining your research question, identifying key variables, and selecting appropriate data collection methods such as surveys, experiments, or existing datasets. Ensure your methods are reliable and valid.

What are some alternatives to using the phrase "necessary data for"?

You can use alternatives such as "required information for", "essential facts for", or "indispensable figures for" depending on the context.

What type of data is considered "necessary data for" analysis?

"Necessary data for" analysis includes all data points directly relevant to your research question or analysis objectives. This could be quantitative, qualitative, or a mix of both, but should exclude extraneous or irrelevant information.

How do I ensure the accuracy of the "necessary data for" my project?

Implement rigorous data validation and cleaning processes. Cross-reference data from multiple sources, use statistical techniques to identify outliers, and maintain detailed records of all data manipulations to ensure accuracy.