hidden variables

USAGE SUMMARY

The phrase "hidden variables" is correct and usable in written English.
It can be used in contexts such as mathematics, statistics, or discussions about theories that involve unobserved factors influencing outcomes. Example: "In quantum mechanics, the debate over hidden variables has significant implications for our understanding of reality."

✓ Grammatically correct

Science

News & Media

Encyclopedias

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

Once the so-called hidden variables controlling reality are exposed, they say, the strangeness of superposition will evaporate.

The New York Times

They consistently found correlations stronger than hidden variables allow.

Science Magazine

That quantum connection produces correlations between the particles that are stronger than hidden variables allow, Bell showed.

Science Magazine

No matter how complex and sophisticated your models, you cannot find hidden variables causing these events to happen.

Huffington Post

The output hidden variables were then estimated through the optimization (see Section 2.2).

Bioinformatics

The unknown (or hidden) variables y are used to represent structural annotations.

DNA Research

Von Neumann also claimed to prove that deterministic "hidden variables" cannot underlie quantum phenomena.

Encyclopedia Britannica

(Ironically, Irish-born physicist John Stewart Bell demonstrated in the mid-1960s that von Neumann's proof was flawed; Bell then fixed the proof's shortcomings, reaffirming von Neumann's conclusion that hidden variables were unnecessary. See also quantum mechanics: Hidden variables).

Encyclopedia Britannica

Soon at least one interpretation, the most common sense version of hidden variables, was completely ruled out.

The New York Times

Both algorithms are non-sequential and extract hidden variables simultaneously.

Open Geospatial Data, Software and Standards

Human-verified similar examples from authoritative sources

Similar Expressions

1 human-written examples

The Kochen-Specker Theorem, the earlier theorem of Gleason (Gleason 1957 and Bell 1966), and other no-hidden-variables results, including Bell's inequality (Bell 1964), show that any hidden-variables formulation of quantum mechanics must be contextual.

SEP

Expert writing Tips

Best practice

When discussing quantum mechanics or statistical modeling, use "hidden variables" to accurately represent underlying, unobserved factors influencing observed phenomena.

Common error

Avoid using "hidden variables" interchangeably with "confounding variables". Confounding variables are known but unaddressed factors, while "hidden variables" are theoretical constructs not directly observed.

Linguistic Context

The phrase "hidden variables" primarily functions as a noun phrase. As supported by Ludwig, it refers to unobservable or theoretical factors that influence observed phenomena, particularly in quantum mechanics and statistical modeling.

Expression frequency: Uncommon

Frequent in

Science

60%

Encyclopedias

20%

News & Media

20%

Less common in

Formal & Business

0%

Wiki

0%

Social Media

0%

Ludwig's WRAP-UP

In summary, "hidden variables" is a noun phrase primarily used in scientific and philosophical contexts, particularly within discussions of quantum mechanics and statistical modeling. As Ludwig AI confirms, it refers to underlying, unobserved factors influencing observed phenomena. Its usage is grammatically correct and most frequently found in scientific literature, encyclopedias, and news media. Be sure to distinguish it from related terms like "confounding variables", and consider alternatives like "latent variables" depending on the specific context. The term carries a formal and scientific register, making it suitable for academic and technical writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

latent variables

Focuses on the statistical aspect of unobserved variables in a model.

unobserved factors

Emphasizes the aspect of factors that are not directly observed or measured.

underlying parameters

Highlights the idea of parameters that exist at a fundamental level.

latent influences

Focuses on the influence of unobserved elements.

unseen determinants

Emphasizes the aspect of determining factors that are not visible.

inferred variables

Highlights the aspect of deducing the variables that have a relationship.

unaccounted elements

Focuses on the idea of the elements that are not considered or counted.

lurking variables

Emphasizes on the aspect of a variable that has a surprising impact.

unmodeled factors

Highlights the idea of factors that are not present in a model.

intrinsic properties

Focuses on the properties of elements that cause an observed behavior.

FAQs

What are "hidden variables" in quantum mechanics?

In quantum mechanics, "hidden variables" refer to hypothetical underlying factors that could explain the probabilistic nature of quantum phenomena, suggesting that quantum states are not fundamentally random but determined by these unobserved variables.

How does the concept of "hidden variables" relate to Bell's theorem?

Bell's theorem and Bell's inequality provide experimental tests to determine whether local "hidden variables" can explain quantum correlations. Experiments have largely refuted local "hidden variables" theories, supporting the non-local nature of quantum mechanics.

What's the difference between "hidden variables" and "latent variables"?

While both terms refer to unobserved factors, "hidden variables" are typically used in physics and philosophy of physics to describe deterministic variables underlying quantum phenomena, whereas "latent variables" are used in statistics and machine learning to model unobserved factors influencing observed data.

Can "hidden variables" be used in statistical modeling?

Yes, the concept can be adapted in statistical modeling, where they might be referred to as "latent variables" or unobserved factors. These variables are inferred from observed data and are used to explain relationships between the observed variables.