Complete the following steps to interpret Power and Sample Size for 2-Level Factorial Design. Key output includes the effect, the number of replicates, the power, the number of center points, the total number of runs, and the power curve.
Using the values of the three power function variables that you entered, Minitab calculates the number of replicates, the effect size, the power of the design, or the number of center points.
If you enter the number of replicates, the power value, and the number of center points, Minitab calculates the effect. The effect is the difference between the means of the response variable at the high and low levels of a factor that you want the design to detect. This difference can be the result of one factor alone (main effect), or of a combination of factors (interaction).
If you enter the number of replicates, the effect size, and the power value, Minitab calculates the number of center points. Center points are experimental runs with all factor levels set halfway between the low and high settings. Center points are mainly used to detect curvature effects, but adding more center points can also increase the power somewhat.
If you enter the effect size, the power value, and the number of center points, then Minitab calculates the number of replicates. Replicates are multiple experimental runs with the same factor settings. Because the number of replicates is an integer value, the actual power may be slightly greater than your target value.
If you increase the number of replicates, the power of your design also increases. You want enough replicates to achieve adequate power.
For every case, Minitab calculates the total runs from the number of replicates and the number of center points. The total number of runs is the product of the number of corner points and the number of replicates, plus the product of the number of center points per block and the number of blocks.
If you enter the number of replicates, the effect size, and the number of center points, Minitab calculates the power value. Power is the probability that you will correctly find a significant effect. A power value of 0.9 is usually considered enough. A value of 0.9 indicates that you have a 90% chance of detecting an effect. Usually, when the total number of runs is smaller or the effect is smaller, then the design has less power.
Step 2: Examine the power curve
Use the power curve to assess the appropriate properties for your design.
The power curve represents the relationship between power and effect size, for every combination of center points and replicates. Each symbol on the power curve represents a calculated value based on the properties that you enter. For example, if you enter a number of replicates, a power value, and a number of center points, then Minitab calculates the corresponding effect size and displays the calculated value on the graph for the combination of replicates and center points. If you solve for replicates or center points, the plot also includes curves for other combinations of replicates and center points that are in the combinations that achieve the target power. The plot does not show curves for cases that do not have enough degrees of freedom to assess statistical significance.
Examine the values on the curve to determine the effect size that the experiment detects at a certain power value, number of corner points, and number of center points. A power value of 0.9 is usually considered adequate. However, some practitioners consider a power value of 0.8 to be adequate. If a design has low power, you might fail to detect an effect that is practically significant. Increasing the number of replicates increases the power of your design. You want enough experimental runs in your design to achieve adequate power. A design has more power to detect a larger effect than a smaller effect.