marginal error

USAGE SUMMARY

The phrase "marginal error" is correct and usable in written English.
It is typically used in statistical or mathematical contexts to refer to a small error or deviation from a true value. Example: "The results of the experiment showed a marginal error of less than 2%, indicating high accuracy in the measurements."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

45 human-written examples

Even a two-shot recovery in the ninth handed the Swiss the crucial final stone in the decisive end, but some fine build-up play by the Britons forced Stoeckli's marginal error.

The Guardian - Sport

We observe that the asymptotic results can predict the finite-system results with marginal error.

EURASIP Journal on Wireless Communications and Networking

Load displacement curves obtained from the numerical results are in good agreement with experiments with a marginal error of 2%.

Composite Structures

A confirmatory test at the optimum conditions resulted in a uranium recovery of 95%, indicating a marginal error of 4.99%.

Annals of Nuclear Energy

Among the three modeling methods, predictions using RSM model were found better in agreement with experimental results with a marginal error between 4% and 8%.

Minerals Engineering

Comparisons showed successful predictions of such a marginal error that could be characterized as quite sufficient for a primary model validation.

Fuel Processing Technology

Human-verified similar examples from authoritative sources

Similar Expressions

15 human-written examples

These marginal errors were acceptable.

Progress in Orthodontics

For simplicity the results of this study were compared with Reverse Osmosis System Analysis (ROSA) software which was assumed to have acceptable marginal errors.

Desalination

For each item, scale errors were calculated following Aiken and Groth-Marnat (2006) and Guest (2000) and marginal errors (i.e., non-modal frequencies) were calculated according to the methods suggested by Guest (2000) and Menzel (1953).

International Journal of STEM Education

Even marginal errors can trigger a catastrophic cascade: Ground movement can create unintended fractures or cracks in brittle formations near old wells that have been improperly abandoned or push fluids into new poorly engineered wells that can shear casings.

Los Angeles Times

These unique results, in addition to the marginal data errors, strengthen the utilization of this 3D device.

Progress in Orthodontics

Expert writing Tips

Best practice

When discussing statistical or experimental results, quantify the "marginal error" whenever possible (e.g., "a marginal error of ±5%") to provide context for the precision of your findings.

Common error

Avoid assuming that a "marginal error" is always inconsequential. Even seemingly small errors can accumulate or have significant impacts in certain contexts, so always consider the potential implications of the error.

Linguistic Context

The phrase "marginal error" functions as a noun phrase, where "marginal" acts as an adjective modifying the noun "error". It refers to the acceptable or tolerable difference between a calculated value and the true value. Ludwig's examples illustrate its usage in scientific, statistical, and even sports contexts.

Expression frequency: Uncommon

Frequent in

Science

85%

News & Media

10%

Wiki

5%

Less common in

Formal & Business

0%

Encyclopedias

0%

Social Media

0%

Ludwig's WRAP-UP

The phrase "marginal error" is a technical term most frequently employed in scientific and statistical contexts to denote an acceptable degree of inaccuracy. Ludwig AI confirms its grammatical correctness and common usage, particularly in research and data analysis. While alternatives like "small error" or "slight deviation" exist, "marginal error" provides a specific nuance related to statistical uncertainty. When using the phrase, it's important to quantify the error (e.g., specify a percentage) and to consider its potential impact, as even a "marginal error" can have consequences in certain applications.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

small error

This alternative uses a more common adjective, "small", to describe the error, making it more accessible.

slight error

This alternative replaces "marginal" with "slight", emphasizing the error's insignificance.

minor inaccuracy

This alternative uses "inaccuracy" instead of "error", softening the negative connotation.

negligible error

This alternative highlights that the error is so small it can practically be ignored.

minimal deviation

This alternative replaces "error" with "deviation", implying a departure from a standard or norm that's very small.

acceptable error

This alternative suggests that the size of the error is within acceptable limits.

tolerable error

This alternative expresses that the error is not ideal but can be tolerated.

insignificant error

This alternative indicates that the error doesn't have a relevant impact on final results.

trivial error

This alternative focuses on how commonplace the error is, almost dismissing it as inconsequential.

modest error

This alternative uses the term "modest" to depict the error as being within expected or reasonable limits.

FAQs

How is "marginal error" calculated in sample size determination?

In sample size calculations, "marginal error" (often denoted as 'd' or 'ME') is the maximum acceptable difference between the sample statistic and the true population parameter. It's used in formulas alongside confidence level and estimated population proportion to determine the necessary sample size.

What is the relationship between confidence interval and "marginal error"?

The "marginal error" is half the width of the confidence interval. A smaller "marginal error" results in a narrower confidence interval, indicating a more precise estimate of the population parameter.

What are some alternatives to "marginal error"?

You can use alternatives like "small error", "slight error", or "minimal deviation" depending on the context.

How does "marginal error" affect the reliability of research results?

A larger "marginal error" increases the uncertainty in research findings, making it more difficult to generalize results to the entire population. Researchers aim for a "marginal error" that balances desired precision with practical constraints like sample size and cost.