mean signed error

USAGE SUMMARY

'mean signed error' is a correctly formed phrase in written English.
The phrase is used to describe a type of mathematical error. For example, a student may calculate the mean signed error of a test to determine where to focus their extra study efforts.

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

12 human-written examples

Mean signed error.

IPSJ Transactions on Computer Vision and Applications

Compared to CIS and TDDFT, there is no systematic bias for the pp-RPA with the mean signed error close to zero.

Duke University

Mean signed error can be useful to identify the tendency for under- or over-estimation of elevations (i.e. bias), while RMSE represents the overall mean elevation accuracy of a DTM.

Carbon Balance and Management

This observed difference between elevation classes became larger with increased levels of data thinning; the mean signed error difference between submontane and montane areas with 20 returns m−2 (0.31 m) increased to 2.64 m when data density dropped to 1 return m−2.

Carbon Balance and Management

The developed method was robust to inter-observer variability and produced very good accuracy — 3.2±1.1 mm absolute surface positioning error, <1 mm mean signed error and <5% mean volume difference.

Ultrasound in Medicine & Biology

Additionally, the MAE and mean signed error (MSE) of each gender and age group in 5-year intervals, which are counted based on the ground truth age and estimated age, respectively, are shown in Figs. 7 and 8, respectively.

IPSJ Transactions on Computer Vision and Applications

Human-verified similar examples from authoritative sources

Similar Expressions

48 human-written examples

That does not mean they signed it.Eh?

The Economist

What was the root mean squared error?

Harvard Business Review

S.E.M.: mean standard error.

BMC Complementary and Alternative Medicine

By EAE9 all animals started to display clinical signs of neurological disability (0.57 ± 0.45; mean ± standard error of the mean (SEM)).

BioMed Research International

We further tested the performance of our adders with and without the sign error correction module in three real applications, mean filter, edge detection, and k-means clustering.

Integration, the VLSI Journal

Expert writing Tips

Best practice

When reporting "mean signed error", always include the units of measurement to provide context for the magnitude of the error. For example, "mean signed error" of 0.5 meters indicates a different level of accuracy than 0.5 millimeters.

Common error

Avoid using "mean signed error" interchangeably with "mean absolute error". "Mean signed error" indicates bias (over or underestimation), while "mean absolute error" measures the magnitude of errors regardless of direction. Choose the metric that best reflects the aspect of error you want to highlight.

Linguistic Context

The phrase "mean signed error" functions as a technical term within statistics and data analysis. As demonstrated by Ludwig, it quantifies the bias in a set of predictions or measurements, indicating whether there is a tendency for overestimation or underestimation. Ludwig AI validates its use in describing errors.

Expression frequency: Common

Frequent in

Science

70%

Academia

20%

News & Media

10%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "mean signed error" is a statistically significant metric predominantly used in scientific and academic fields to measure the bias in predictions or measurements. As Ludwig AI confirms, it is a grammatically sound and commonly used term, helping to determine if a model systematically over- or underestimates values. Its usage is characterized by a formal tone and a clear objective: to assess directional accuracy. Keep in mind that "mean signed error" differs significantly from "mean absolute error", as the former indicates bias, while the latter indicates magnitude regardless of direction. Understanding and correctly applying "mean signed error" is crucial for rigorous data analysis and technical reporting.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

average signed deviation

Replaces "mean" with "average" and "error" with "deviation", focusing on the typical signed difference.

signed bias

Condenses the phrase to highlight the presence and direction of systematic over- or underestimation.

average error with sign

Rephrases to emphasize that the sign of the error is being considered in the average.

mean algebraic error

Uses "algebraic" to specify that the sign is crucial in the error calculation.

signed error average

Swaps the order of "error" and "average".

systematic error component

Focuses on the systematic aspect of the error, implying a consistent directional bias.

directional error bias

Highlights the directional bias present in the errors.

average signed residual

Uses "residual" instead of "error", common in regression analysis.

signed difference mean

Rephrases, highlighting it's a difference that has sign.

mean error with direction

Emphasizes the directional aspect of the error.

FAQs

What does a "mean signed error" close to zero indicate?

A "mean signed error" close to zero suggests that, on average, the model's overestimations and underestimations balance each other out, indicating no systematic bias in the predictions.

How is "mean signed error" useful in evaluating digital terrain models (DTMs)?

"Mean signed error" can help identify whether a DTM tends to over- or under-estimate elevations, revealing potential systematic biases in the model's construction or data acquisition.

What's the difference between "mean signed error" and root mean square error (RMSE)?

"Mean signed error" indicates the tendency of a model to over- or under-estimate, while root mean square error (RMSE) measures the overall magnitude of the errors, regardless of their direction. RMSE is always non-negative, while "mean signed error" can be positive or negative.

When should I use "mean signed error" versus "mean absolute error" (MAE)?

Use "mean signed error" when you want to assess the bias or directional accuracy of a model. Use "mean absolute error" (MAE) when you are interested in the overall magnitude of the errors without regard to their direction.