Power Analysis and Sample Size Calculation in Experimental Design - Calculating Power

Properly designed experiments must ensure that power will be reasonably high to detect reasonable departures from the null hypothesis. Otherwise, an experiment is hardly worth conducting. Elementary textbooks contain detailed discussions of the factors influencing power in a statistical test. These include:

  1. What kind of statistical test is being performed. Some statistical tests are inherently more powerful than others.

  2. Sample size. In general, the larger the sample size, the larger the power. However, generally increasing sample size involves tangible costs, both in time, money, and effort. Consequently, it is important to make sample size "large enough," but not wastefully large.

  3. The size of experimental effects. If the null hypothesis is wrong by a substantial amount, power will be higher than if it is wrong by a small amount.

  4. The level of error in experimental measurements. Measurement error acts like "noise" that can bury the "signal" of real experimental effects. Consequently, anything that enhances the accuracy and consistency of measurement can increase statistical power.