alpha level

USAGE SUMMARY

The phrase "alpha level" is correct and usable in written English.
It is typically used in statistical contexts to refer to the threshold for significance in hypothesis testing. Example: "In our study, we set the alpha level at 0.05 to determine statistical significance."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Alpha level set a priori at 0.05.

Journal of Science and Medicine in Sport

The alpha level (type I error) was set at 0.05.

Nature

The nominal alpha level for these comparisons was 0.05.

Nature

Alpha level of 0.05 was used for statistical tests.

Nature

The overall reliability was established at 0.717 alpha level.

Procedia - Social and Behavioral Sciences

An alpha level of 0.05 was used for all statistical tests.

Nature

The alpha level was 5%.

European Transport Research Review

Alpha level was set at.05.

Asian-Pacific Journal of Second and Foreign Language Education

An alpha level of.05 was used.

Security Informatics

Alpha level was set at 0.05.

Plosone

In all tests, alpha level was 0.05.

Plosone

Expert writing Tips

Best practice

When reporting statistical results, clearly state the "alpha level" used to determine statistical significance. This transparency enhances the reproducibility and interpretability of your findings.

Common error

Avoid interchanging the "alpha level" with the p-value. The "alpha level" is a pre-set threshold, while the p-value is calculated from the data. Do not claim the "alpha level" was 'calculated'.

Linguistic Context

The phrase "alpha level" functions as a noun phrase within the context of statistical analysis. As shown in Ludwig, it usually refers to the predetermined threshold for statistical significance in hypothesis testing.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "alpha level" is a common and grammatically sound term in statistical analysis, representing the threshold for statistical significance. Predominantly used in scientific contexts, as confirmed by Ludwig, it signifies the acceptable risk of rejecting a true null hypothesis. Alternative phrases include "significance level" and "Type I error rate", but "alpha level" is the most recognized. Remember to clearly state the "alpha level" in research reports to maintain transparency and avoid confusion with the p-value.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Significance level

This term is often used interchangeably with "alpha level" in statistical hypothesis testing, indicating the probability of rejecting the null hypothesis when it is true.

P-value threshold

Refers to the predetermined cutoff for the p-value to determine statistical significance, very close but with a slight difference.

Type I error rate

Describes the probability of making a Type I error (false positive), related because it identifies the risk of rejecting a true null hypothesis.

Critical probability

Denotes the probability below which the null hypothesis is rejected, keeping a similar concept.

Rejection region

Indicates the range of values for which the null hypothesis is rejected, so related but with a different approach.

Significance criterion

The established standard for determining whether a result is statistically significant.

Error probability

General term for the likelihood of making an error in statistical inference, which could be related with the study itself and the hypothesis.

Confidence threshold

While related to confidence intervals, it indirectly affects the "alpha level" by determining the margin of error.

Statistical boundary

It's a related term for defining the point at which results are considered statistically significant, more conceptually related.

Test level

General term for establishing the parameters of a specific test or analysis.

FAQs

What is the "alpha level" in statistics?

The "alpha level", also known as the significance level, is the probability of rejecting the null hypothesis when it is actually true. It's a pre-set threshold used to determine statistical significance.

How do I choose an appropriate "alpha level"?

The choice of "alpha level" depends on the context of the study. Common values are 0.05 (5%) and 0.01 (1%). A lower "alpha level" reduces the risk of a false positive but increases the risk of a false negative.

What's the difference between the "alpha level" and the p-value?

The "alpha level" is a predetermined threshold for statistical significance, while the p-value is the probability of obtaining results as extreme as, or more extreme than, the observed results, assuming the null hypothesis is true. If the p-value is less than the "alpha level", the results are considered statistically significant.

Can I use a different phrase instead of "alpha level"?

You can use alternatives like "significance level" or "Type I error rate", although "alpha level" is widely accepted and understood in statistical contexts.