A Type I error occurs when a true null hypothesis is incorrectly rejected. It is also known as a 'false positive' or 'alpha error'.
5 Must Know Facts For Your Next Test
The probability of committing a Type I error is denoted by $\alpha$, which is the significance level of the test.
Common values for $\alpha$ are 0.05, 0.01, and 0.10, representing the risk of making a Type I error.
Reducing the significance level $\alpha$ decreases the likelihood of a Type I error but increases the risk of a Type II error.
Type I errors can lead to incorrect conclusions that there is an effect or difference when there isn't one.
In hypothesis testing, controlling the Type I error rate is crucial to maintaining the integrity of statistical conclusions.
Review Questions
What does it mean to commit a Type I error in hypothesis testing?
How does changing the significance level $\alpha$ affect the probability of making a Type I error?
Why is controlling for Type I errors important in statistical analysis?
Related terms
Type II Error: Occurs when a false null hypothesis is not rejected, also known as a 'false negative' or 'beta error'.
$p$-Value: The probability of obtaining test results at least as extreme as those observed during the test, assuming that the null hypothesis is true.
$\alpha$ Level (Significance Level): $\alpha$ represents the threshold at which we reject the null hypothesis; it defines our tolerance for committing a Type I error.