significance threshold

USAGE SUMMARY

The phrase "significance threshold" is correct and usable in written English.
It is most commonly used to refer to the level of statistical significance that needs to be reached in order to draw a valid conclusion from a study. For example, "The significance threshold for this survey was set at p < 0.05."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Significance threshold for cleansing data during iterations.

Harvard University

The significance threshold was set at p < 0.05.

Nature

The significance threshold (p) was set below 0.05 (MudPIT scoring).

Nature

Statistical significance threshold was set to 0.05 for all tests.

Nature

We set the typical genome-wide significance threshold as a significance threshold of our study (P < 5.0 × 10−8)210−8

Nature

Positions crossing the statistical significance threshold are indicated and their level of statistical significance shown.

Nature

The blue horizontal line in each plot correspond p-value of 0.05 significance threshold.

Nature

A temporal shuffling procedure was used to estimate the significance threshold (dashed line, see methods).

Nature

All significance values were two-tailed, with a significance threshold of P<0.05.

Nature

For the work presented here, we have used an e-value significance threshold of ≤0.05.

Nature

wavdetect has options for setting significance threshold (false source rate) and user supplied background.

Harvard University

Expert writing Tips

Best practice

When reporting statistical results, clearly state the "significance threshold" used (e.g., p < 0.05) to ensure transparency and reproducibility.

Common error

Avoid assuming that results exceeding the "significance threshold" are automatically practically important. Statistical significance indicates the unlikeliness of the result occurring by chance, but practical significance depends on the magnitude of the effect and its real-world implications.

Linguistic Context

The phrase "significance threshold" functions as a noun phrase that identifies a predetermined level used to determine if a result is statistically significant. As Ludwig indicates, it is mainly related to academic and scientific contexts. Reaching this "significance threshold" indicates that a result is unlikely to have occurred by chance.

Expression frequency: Very common

Frequent in

Science

70%

Academia

25%

Formal & Business

5%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The term "significance threshold" is a crucial concept in research, particularly in scientific and academic fields. As Ludwig points out, this phrase, which functions as a noun phrase, defines the predetermined level at which results are considered statistically significant, typically expressed as a p-value (e.g., p < 0.05). The primary contexts for its use are scientific publications and academic papers, where it serves to establish an objective criterion for evaluating the validity of research findings. Remember to clearly state the threshold when presenting results and avoid confusing statistical and practical significance. Related phrases, such as "statistical significance level" or "critical value", offer alternative ways to convey this meaning, but maintain precision and clarity.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

statistical significance level

This alternative specifies the statistical nature of the threshold, emphasizing its role in statistical analysis.

critical value

This term is often used in hypothesis testing to denote the boundary beyond which results are considered statistically significant.

alpha level

"Alpha level" is a direct synonym in the context of statistical hypothesis testing, representing the probability of rejecting the null hypothesis when it is true.

rejection region

This phrase refers to the range of values for which the null hypothesis is rejected, implying the existence of a significance threshold.

cut-off value

This is a more general term applicable in various fields, not exclusively statistical, to denote a threshold for categorization or decision-making.

minimum requirement

This shifts the focus to the minimum standard that must be met, which can be considered a threshold for acceptance or validity.

acceptable limit

This term suggests a boundary of tolerance or permissibility, similar to a threshold for what is considered acceptable.

level of confidence

This relates to the degree of certainty required before a result is deemed significant, implying a threshold for confidence.

margin of error

While technically different, the "margin of error" defines a range around a result, implying a threshold for acceptable deviation.

detection limit

This term is used when assessing or measuring something and establishing at what point it can reliably be detected.

FAQs

How is the "significance threshold" typically determined in scientific research?

The "significance threshold" is often set at p < 0.05, meaning there is less than a 5% chance that the results occurred due to random chance. More stringent thresholds, such as p < 0.01 or p < 0.001, may be used depending on the field and the need to minimize false positives.

What does it mean when results do not meet the "significance threshold"?

If results do not meet the "significance threshold", it suggests that there is not enough evidence to reject the null hypothesis. This does not necessarily mean the null hypothesis is true, but rather that the study did not provide sufficient evidence against it. Further research with larger sample sizes or different methodologies may be needed.

What are some alternatives to using a fixed "significance threshold"?

Alternatives to a fixed "significance threshold" include reporting p-values directly and allowing readers to interpret the strength of evidence for themselves, or using Bayesian methods that provide probabilities of hypotheses being true. Another approach is to use methods to control the False Discovery Rate (FDR), which adjust the threshold to account for multiple comparisons.

How does sample size affect the "significance threshold"?

Larger sample sizes increase the statistical power of a study, making it easier to detect smaller effects and achieve statistical significance. With very large sample sizes, even trivial effects can surpass the "significance threshold", highlighting the importance of also considering the practical significance of the findings.