Nominal Significance Level
- Sets the tolerated probability of falsely rejecting a true null hypothesis (Type I error).
- Commonly chosen values are 0.05 or 0.01, corresponding to 5% or 1% risk of a Type I error.
- It is a threshold for deciding significance, distinct from the p-value computed from data.
Definition
Section titled “Definition”The nominal significance level, also called the alpha level (often written as ), is the probability of making a Type I error in statistical analysis — that is, the probability that a statistical test rejects the null hypothesis when the null hypothesis is actually true. Typical choices for the nominal significance level are 0.05 or 0.01.
Explanation
Section titled “Explanation”A Type I error occurs when a test indicates an effect (rejects the null hypothesis) even though no effect exists. The nominal significance level specifies the chance of this error occurring before data are observed. It acts as a pre-specified threshold used to interpret the result of a statistical test.
The nominal significance level should not be confused with the probability that the null hypothesis is true or false. The probability that the null hypothesis is true or false is described in the source text as the p-value, which is calculated based on the data collected in the study.
Examples
Section titled “Examples”Medication and blood pressure
Section titled “Medication and blood pressure”A researcher tests whether a new medication reduces blood pressure. The null hypothesis is that the medication has no effect; the alternative is that it does have an effect. If the researcher reports effectiveness at a significance level of 0.05, this means there is a 5% chance the observed result was due to chance and that the medication actually has no effect on blood pressure.
Education level and income
Section titled “Education level and income”A study examines the relationship between education level and income. The null hypothesis is that there is no relationship; the alternative is that there is a relationship. If the test is significant at a significance level of 0.01, this means there is a 1% chance the observed result was due to chance and there is no actual relationship between education level and income.
Notes or pitfalls
Section titled “Notes or pitfalls”- The nominal significance level is a pre-specified probability of making a Type I error and is not the same as the p-value.
- According to the source content, the p-value is described as the probability that the null hypothesis is true or false, and it is calculated from the collected data.
Related terms
Section titled “Related terms”- Type I error
- Alpha level
- p-value
- Null hypothesis
- Alternative hypothesis