Power Calculation Simulation

Statistical power is a crucial concept in regression analysis that helps us understand the probability of correctly rejecting a false null hypothesis. In other words, it measures our ability to detect a true effect when it exists. Understanding power is essential for designing studies and interpreting results.

1. Power Definition: Power is the probability of rejecting the null hypothesis when the alternative hypothesis is true. A higher power means we have a better chance of detecting a real effect if it exists.

2. Factors Affecting Power:

• Sample Size: Larger samples generally lead to higher power

• Effect Size: Larger effects are easier to detect

• Significance Level: The chosen alpha level affects power

• Variability: Less variability in the data increases power

3. Power Analysis: Before conducting a study, power analysis helps determine the required sample size to achieve desired power levels. After a study, it helps interpret negative results by assessing whether the study had sufficient power to detect meaningful effects.

The following simulation allows you to explore how these factors affect statistical power and helps you determine the appropriate sample size for your study: