Type I Error
In inferential statistics, a Type I error occurs when a null hypothesis (H0) is incorrectly rejected even though it is true.
Simply put, it's a false positive. The analysis suggests there's a relationship between the two variables when, in fact, none exists.
The null hypothesis (H0) asserts that no relationship exists between the variables: for instance, "there is no difference between groups" or "the variables are independent."
This hypothesis is rejected when the independence test indicates otherwise.
A Type I error happens when a statistical test, like the chi-square test, leads to the rejection of the null hypothesis, suggesting an effect or a relationship when, in reality, the observed difference is simply due to random variation.
The probability of making a Type I error is represented by the significance level (α), which is set before performing the statistical test.
For example, if a significance level of α=0.05 is chosen, there is a 5% chance of making a Type I error, meaning rejecting the null hypothesis when it is actually true.
Thus, the significance level reflects the risk of concluding that there is an effect or a relationship between two variables when, in fact, none exists.
And so on.
