significance level

USAGE SUMMARY

"significance level" is a correct and usable phrase in written English.
Generally, it's used in research contexts to indicate the probability of an observed result being due to chance. For example, "After analyzing the data, we found that the results were significant at the 0.05 significance level."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

The significance level was set to 0.05.

Nature

The significance level was set at 0.05.

Nature

Significance level was set at P < 0.05.

Nature

Significance level was set at p < 0.05.

Nature

The significance level was α =.2.

American Journal of Obstetrics and Gynecology

A significance level of 0.05 was considered.

Procedia Manufacturing

Significance level was 5% through the analysis.

Procedia - Social and Behavioral Sciences

Significance level was always set to 0.05 (two sided).

Nature

The significance level was limited to 5% (p < 0.05).

Nature

probability (significance level).

Heritage Science

Precision, at significance level 95%.

Complex & Intelligent Systems

Expert writing Tips

Best practice

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

Common error

Avoid interpreting statistical significance (indicated by the "significance level") as proof of real-world importance. A statistically significant result might have a small effect size and limited practical implications.

Linguistic Context

The phrase "significance level" functions as a noun phrase that specifies a pre-determined threshold for statistical hypothesis testing. Ludwig AI confirms its common usage in scientific and research contexts.

Expression frequency: Very common

Frequent in

Science

98%

News & Media

1%

Formal & Business

1%

Less common in

Academia

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "significance level" is a very common noun phrase used primarily in scientific and research contexts to denote the threshold for statistical significance. Ludwig AI validates its correct usage, with a strong emphasis on its role in academic and scientific writing. It represents the probability of rejecting a true null hypothesis and is crucial for interpreting research findings accurately. While alternative phrases exist, such as "alpha level", the core concept remains consistent across scientific disciplines.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

alpha level

Replaces "significance level" with its symbolic representation, commonly used in statistical notation.

level of significance

Reorders the words in "significance level" while retaining the core meaning.

statistical significance threshold

Uses "threshold" to emphasize the cutoff point for determining statistical significance.

critical probability value

Replaces "significance" with "critical probability", highlighting the probabilistic aspect.

p-value threshold

Focuses on the p-value as the measure compared against the significance criterion.

Type I error rate

Emphasizes the risk of making a false positive conclusion.

risk of false positive

Highlights the potential for incorrectly rejecting the null hypothesis.

confidence threshold

Shifts focus slightly to the level of confidence in the results, inversely related to the significance level.

statistical error margin

Highlights the acceptable margin of error in statistical inference.

acceptable error probability

Directly states the likelihood of making an incorrect statistical inference.

FAQs

How is the "significance level" typically represented in research papers?

The "significance level" is commonly represented by the Greek letter alpha (α) and is often set to 0.05, meaning there's a 5% risk of concluding that a difference exists when it does not.

What does it mean when results are described as statistically significant at a "significance level" of 0.01?

A "significance level" of 0.01 indicates that there is only a 1% chance of observing the results if the null hypothesis were true. This suggests stronger evidence against the null hypothesis than a "significance level" of 0.05.

How does the choice of "significance level" affect the outcome of a statistical test?

The "significance level" determines the threshold for rejecting the null hypothesis. A lower "significance level" (e.g., 0.01) makes it harder to reject the null hypothesis, reducing the risk of a false positive but increasing the risk of a false negative.

What are some alternative ways to refer to the "significance level" in statistical reporting?

Instead of "significance level", you could use phrases like "alpha level" or "statistical significance threshold", although these might be more suited for technical audiences.