too few variables

USAGE SUMMARY

The phrase "too few variables" is correct and usable in written English.
It can be used when discussing a situation where there are not enough variables to draw a meaningful conclusion or to conduct a thorough analysis. Example: "The results of the experiment were inconclusive due to too few variables being tested."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified examples from authoritative sources

Exact Expressions

2 human-written examples

These are two shallow as they can harbour too few variables, do not put studied phenomena in their proper context, and sweep persona under the carpet".

Journal of Global Entrepreneurship Research

Since SRHS is a useful indicator in health sciences, this inquiry has contributed to a reversal of two trends in the published literature: studies either employing too few variables, ultimately resulting in a constricted view and scope of self-rated perceptions of health, or alternatively, focusing predominantly on elderly populations.

BMC Public Health

Human-verified similar examples from authoritative sources

Similar Expressions

57 human-written examples

For example, a single gene might have too few variable nucleotide sites to resolve very similar species or ecotypes.

Bioinformatics

We used univariable analyses to screen potential variables because we anticipated having too few events to include all variables in the initial model.

BMC Nephrology

The primary independent variable, TCDD, was modeled as a continuous (log) variable (too few subjects in the preterm category prevented analysis by TCDD categories).

Environmental Health Perspectives

The few studies that have undertaken multivariable regression analysis provide unreliable estimates because they did not include all the known independent clinical variables or the analysis had too few events for the number of variables assessed.

BMJ Open

As the number of potential structures is super-exponential in the number of nodes, even systems with few variables have too many possible network structures to allow for an exhaustive search.

BioData Mining

The increased error in estimating the mean and standard deviations of the step kinematic variables with too few steps can impose an experimental cost with regard to statistical design considerations.

Journal of Biomechanics

This method was not applied for the WST because of too few observations of the respective variables (unpublished  =  0 of 164; German language  =  17 of 164).

Plosone

Categories of categorical variables with too few observations were amalgamated when biological, or logical, new categories were possible to make.

Acta Veterinaria Scandinavica

Stratified analyses were performed according to other variables; however, too few studies did so to enable robust investigation of these by meta-analysis.

British Journal of Cancer

Expert writing Tips

Best practice

When discussing research or analysis, ensure you justify why the number of variables is limited, and acknowledge potential impacts on the conclusions drawn.

Common error

Avoid drawing definitive conclusions when your analysis is based on "too few variables". Instead, acknowledge the limitations and suggest further research with a more comprehensive set of variables.

Linguistic Context

The phrase "too few variables" functions as a modifier describing the quantity of variables. It suggests a deficiency that affects the validity or completeness of an analysis or study. Ludwig confirms its grammatical correctness and usability.

Expression frequency: Common

Frequent in

Science

60%

News & Media

20%

Formal & Business

20%

Less common in

Academia

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, the phrase "too few variables" is a grammatically sound and frequently used expression, particularly within scientific and academic writing. According to Ludwig, it's used to denote a deficiency in the number of variables considered, which can undermine the robustness of analysis or research. While generally appropriate for formal contexts, its use signals a potential limitation and calls for cautious interpretation of results. Alternatives such as "insufficient variables" or "limited number of variables" can be used to convey similar meaning. Recognizing this phrase and its implications helps writers craft more precise and critical analyses.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

not enough variables

A more direct and informal way of saying there are too few variables.

insufficient variables

Replaces "too few" with a single adjective meaning not enough.

limited number of variables

Emphasizes the restricted quantity of variables.

inadequate variables

Uses a synonym for "insufficient" to convey the lack of needed variables.

scarce variables

Highlights the rarity or limited availability of variables.

paucity of variables

Uses a more formal noun to denote a small quantity of variables.

lack of sufficient variables

More explicitly states the absence of enough variables.

variables are lacking

Shifts the sentence structure to emphasize the missing variables.

variables deficiency

Uses a noun to describe a lack of variables.

underrepresentation of variables

Implies that the number of variables is lower than expected or needed.

FAQs

How can I use "too few variables" in a sentence?

You can use "too few variables" to describe situations where the number of variables is insufficient for a thorough analysis. For example, "The study's conclusions were limited by too few variables being considered".

What are some alternatives to "too few variables"?

Some alternatives include "insufficient variables", "limited number of variables", or "inadequate variables", depending on the context.

What is the impact of having "too few variables" in a research study?

Having "too few variables" can lead to an oversimplified view of the phenomenon under study, potentially resulting in inaccurate or incomplete conclusions. It may also limit the generalizability of the findings.

Is it always a problem to have "too few variables"?

While it's generally desirable to include all relevant variables, sometimes simplifying a model with "too few variables" can be useful for initial exploration or when data is limited. However, it's crucial to acknowledge the potential limitations and biases introduced by such simplification.