Statistical Power Calculator

Step-by-Step Guide to Using the Statistical Power Calculator

Understanding the Inputs

To use the Statistical Power Calculator effectively, start by gathering all necessary information. The calculator requires input for the significance level, sample size, effect size, and test type. These variables are critical components in calculating statistical power and related values.

Entering Input Values

• Significance Level (α): Enter your desired significance level, typically denoted as α. This value represents the probability of rejecting the null hypothesis when it is true. Common values include 0.05 for a 5% significance level. Ensure your input is between 0.001 and 0.999, in steps of 0.001.
• Sample Size (n): Provide the number of observations or samples in your study. The sample size should be at least 2 and can be entered in whole numbers.
• Effect Size (d): Input the expected effect size, a measure of the strength of the relationship between variables. This value should be between 0.01 and 5, with increments of 0.01.
• Test Type: Select whether you are conducting a one-tailed or two-tailed test. This choice affects the critical regions of the distribution used in hypothesis testing.

Understanding the Results

After inputting the necessary data, the calculator will compute several key metrics for your analysis:

• Statistical Power (1-β): The calculator computes the probability (expressed as a percentage) that your test will correctly reject a false null hypothesis. A power of 80% or greater is commonly desired for reliable results.
• Type II Error (β): This is the complement of statistical power and reflects the probability of failing to reject a false null hypothesis. The calculator provides this value as a percentage.
• Critical Value: The calculator determines the critical value of the test statistic, which aids in decision-making. This value is presented with up to four decimal places.
• Recommended Sample Size: If you are planning a study, the calculator can suggest an appropriate sample size to achieve sufficient power. It utilizes a standard power level of 80% to give this recommendation, which appears as a whole number.

Conclusion

By properly using these steps and inputs, you can efficiently utilize the Statistical Power Calculator to support your research design and ensure robust statistical results. Remember to critically evaluate each output within the context of your specific study parameters and research field conventions.