What is the difference between alpha level and critical values??
Does the alpha level just help determine the critical value?
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The alpha level and the critical value are terms used in hypothesis testing, particularly in statistics. While they are related, they serve different purposes.
The alpha level, also known as the significance level, is the predetermined threshold used to assess the strength of evidence against the null hypothesis. It represents the maximum probability of making a Type I error, which is rejecting the null hypothesis when it is actually true. Commonly chosen alpha levels are 0.05 (5%) and 0.01 (1%).
On the other hand, critical values are specific values or boundaries on a test statistic, which are used to determine whether to reject or fail to reject the null hypothesis. The critical value is calculated based on the chosen alpha level and the distribution of the test statistic.
To find the critical value, you need to specify the desired alpha level, select the appropriate statistical test, and determine the degrees of freedom or sample size associated with the test. Then, based on the distribution of the test statistic (e.g., z-distribution for normal data, t-distribution for small sample sizes), you can either use statistical tables or software to find the critical value that corresponds to the chosen alpha level.
In summary, the alpha level is a predetermined threshold that guides the decision-making process in hypothesis testing, while the critical value is the specific value or range on the test statistic that determines whether to reject or fail to reject the null hypothesis. The alpha level is used to determine the critical value, but they are not the same thing.