What factors affect the statistical power of a test?

What factors affect the statistical power of a test?

• A. Effect size, sample size, significance level, and variability.
• B. Only the number of predictors in a model.
• C. The color of the data visualization.
• D. The unit of measurement.

Answer: (A)

Power reflects how likely a test is to detect a real effect. The factors that shape this are effect size, sample size, the chosen significance level, and data variability. A larger true effect makes it easier to observe a difference, so power increases with bigger effect sizes. More observations, i.e., a larger sample, reduce sampling error and sharpen the estimate, boosting power. The significance level (alpha) sets how readily you call a result “statistically significant”; a higher alpha makes it easier to declare significance, increasing power but also raising the chance of a false positive. More noise or variability in the data makes the real effect harder to pick out, reducing power. So these four elements directly influence power. Choices about the number of predictors, color of a plot, or the exact unit of measurement don’t by themselves determine power, though modeling and measurement choices can affect variability and precision, which in turn can influence power.

Power reflects how likely a test is to detect a real effect. The factors that shape this are effect size, sample size, the chosen significance level, and data variability. A larger true effect makes it easier to observe a difference, so power increases with bigger effect sizes. More observations, i.e., a larger sample, reduce sampling error and sharpen the estimate, boosting power. The significance level (alpha) sets how readily you call a result “statistically significant”; a higher alpha makes it easier to declare significance, increasing power but also raising the chance of a false positive. More noise or variability in the data makes the real effect harder to pick out, reducing power. So these four elements directly influence power. Choices about the number of predictors, color of a plot, or the exact unit of measurement don’t by themselves determine power, though modeling and measurement choices can affect variability and precision, which in turn can influence power.