α Alpha Level (Significance Level)
The significance level α
- the probability of making the wrong decision when the null hypothesis is true.
- Alpha levels (sometimes just called “significance levels”) are used in hypothesis tests.
- Usually, these tests are run with an alpha level of .05 (5%)
- but other levels commonly used are .01 and .10.
| Term | Formula | |
|---|---|---|
| Precision | PPV, Positive predictive value | True positive/Test outcome positive |
| Accuracy | (true positive + true negative)/Toral Population | |
| Sensitivity | True positive rate | True positive/Condition positive |
| Specificity | True negative rate | True negative/Condition negative |
| Prevalence | Condition positive/Total population | |
Precision(PPV, Positive predictive value)
Precision = True positive/Test outcome positive
Accuracy (ACC):
Accuracy = (true positive + true negative)/Toral Population
Alpha levels and beta levels are related:
- alpha level: usually just called alpha(α)
- the probability of a type I error
- rejecting the null hypothesis when it is true.
- the probability of a type I error
- beta level: usually just called beta(β)
- the probability of a type II error
- accepting the null hypothesis when it’s false.
- the incorrect conclusion that there is no statistical significance (if there was, you would have rejected the null).
- the probability of a type II error
- alpha level: usually just called alpha(α)