The Critical Values Calculator helps users determine critical values, p-values, and rejection regions for specified confidence levels, degrees of freedom, and test types in statistical hypothesis testing.
Critical Values Calculator
Use Our Critical Values Calculator
How to Use the Critical Values Calculator
This step-by-step guide will help you navigate the usage of the Critical Values Calculator, enabling you to calculate critical values and related statistical measures effectively.
Step 1: Select the Confidence Level
Begin by selecting the confidence level for your test, which is essential in determining the statistical confidence of your results. The available options are:
- 90%
- 95%
- 99%
Choose one from the dropdown menu. This value is required, and you must select one to proceed with the calculations.
Step 2: Enter Degrees of Freedom
The next step is to enter the degrees of freedom associated with your statistical test. Degrees of freedom typically depend on your sample size or number of categories in relation to your overall data set.
- The input must be a number between 1 and 1000.
- Ensure your input is valid with increments of 1.
This field is also required to proceed with the critical value calculation.
Step 3: Select the Test Type
Choose the test type that matches your statistical analysis from the following options:
- Two-tailed
- One-tailed
This selection will determine how the critical value is calculated, and you must choose an option to continue.
Step 4: Interpret the Results
Once the necessary inputs are provided, the calculator will compute various critical statistical outputs:
- Critical Value: This is computed based on your test type and confidence level, showing the threshold beyond which you would reject the null hypothesis.
- P-Value: This reflects the probability of obtaining a test statistic at least as extreme as the one observed, under the assumption that the null hypothesis is true.
- Rejection Region: Displays the range of values where the null hypothesis would be rejected.
These results are crucial for determining the significance of your statistical test and make informed data-driven decisions.