control for discrepancies

USAGE SUMMARY

The phrase "control for discrepancies" is correct and usable in written English.
It is typically used in research or analysis contexts to indicate the process of accounting for or adjusting variables that may cause differences in results. Example: "In our study, we need to control for discrepancies in the data to ensure accurate conclusions."

✓ Grammatically correct

Science

Wiki

News & Media

Human-verified examples from authoritative sources

Exact Expressions

2 human-written examples

All samples, including controls and standards, were done in triplicate to control for discrepancies in pipetting.

Plosone

Although participants and non-participants were similar in socio-economic characteristics and number of previous CVD hospitalisations, we could not control for discrepancies in life style.

Global Health Action

Human-verified similar examples from authoritative sources

Similar Expressions

56 human-written examples

In addition to controlling for discrepancies in the indicator set, our weighting system allowed a precautionary approach by reducing the risk of missing a decrease in the state of a major ecosystem.

Plosone

To control for possible discrepancies in laboratory conditions across blocks, the ancestor was always assayed in parallel with each set of lines scored.

G3: Genes, Genomes, Genetics

To control for major discrepancies in the quality and the amount of medical information between the main HMO patients and other HMO patients, a dichotomous variable was created.

BMC Medical Informatics and Decision Making

Limitations were imposed on the data to control for known discrepancies in the logger data, for example, contacts less than 1 s were removed as they can erroneously occur when two loggers are at the edge of their detection zones (Prange et al. 2006).

Behavioral Ecology and Sociobiology

Although we have controlled for the discrepancy in cell numbers between the different regions (see Methods), it is possible that the transcription pattern obtained in mushroom bodies reflects the multimodality of neurons and sensory processing in this part of the CNS.

Plosone

In addition, the desired current discrepancy predicted leader member exchange, job satisfaction, and organizational commitment, even after controlling for the prototype current discrepancy.

The Leadership Quarterly

A further 160 samples selected randomly for quality control exhibited no discrepancies between genotyping results obtained with both methods.

Breast Cancer Research

Finally, possible explanations for discrepancies are explored.

Health Economics Review

(Additional reasons for discrepancies are discussed later).

Environmental Science & Technology

Expert writing Tips

Best practice

When writing scientific reports, clearly state how you "control for discrepancies". Providing specific details about the methods used enhances the credibility and transparency of your research.

Common error

Avoid using "control for discrepancies" without specifying the actual methods or statistical techniques employed. Broad statements without methodological support can weaken your analysis and make it difficult for others to replicate your results.

Linguistic Context

The phrase "control for discrepancies" functions primarily as a verb phrase, indicating an action taken to minimize or adjust for inconsistencies. As Ludwig AI underlines, this term is often found in research and analytical contexts.

Expression frequency: Common

Frequent in

Science

50%

News & Media

25%

Wiki

25%

Less common in

Formal & Business

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "control for discrepancies" is a verb phrase commonly used to describe the process of minimizing or adjusting for inconsistencies in data, primarily in scientific, analytical, and research contexts. As verified by Ludwig AI, the phrase is grammatically correct and finds frequent use in professional writing. Effective use involves clearly specifying the methods employed to achieve accuracy and reliability. Alternatives such as "account for differences" or "adjust for inconsistencies" offer nuanced ways to express similar concepts, with "control" suggesting a more active approach to minimizing variations.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

adjust for inconsistencies

Using "adjust" implies a more direct modification to data or procedures to reduce the impact of differing elements.

normalize for differences

"Normalize" suggests a standardization process to make data comparable despite original dissimilarities.

account for differences

This alternative shifts the focus from active control to simply acknowledging and explaining the presence of variations.

standardize for variations

Using "standardize" emphasizes the application of uniform standards to negate the impact of diversity.

correct for deviations

This phrase specifies that the adjustments aim to rectify departures from an expected norm.

compensate for disparities

This alternative implies that action is taken to offset inequalities between compared items.

mitigate variations

The term "mitigate" suggests reducing the effect of variations, rather than completely eliminating them.

reconcile differences

This option targets the resolution of disagreements or inconsistencies, aiming to align differing aspects.

harmonize inconsistencies

Employing "harmonize" indicates an effort to blend diverse elements into a congruent whole.

factor in anomalies

"Factor in" broadens the scope to include any anomalies, indicating a consideration of irregularities.

FAQs

How is "control for discrepancies" typically used in research?

In research, "control for discrepancies" refers to the methods used to minimize or account for differences in data that could skew results. This might involve using statistical techniques, standardizing procedures, or implementing rigorous quality control measures.

What are some methods to "control for discrepancies" in experiments?

Common methods include randomization, blinding, using "control groups", and applying statistical adjustments such as regression analysis. The specific approach depends on the nature of the experiment and the potential sources of variability.

What's the difference between "control for discrepancies" and "account for discrepancies"?

"Control for discrepancies" implies actively minimizing the impact of variations, while "account for differences" suggests acknowledging and explaining the presence of variations without necessarily eliminating them. The choice depends on whether the goal is to eliminate or simply understand the impact of the discrepancies.

In what contexts is it most important to "control for discrepancies"?

It is crucial to "control for discrepancies" in any context where accuracy and reliability are paramount, such as scientific research, financial analysis, and medical studies. Failing to do so can lead to inaccurate conclusions and flawed decision-making.