we conducted post hoc power analyses using GPower (Faul & Erdfelder, 1992. Most medical literature uses a beta cut-off of 20% (0.2) - indicating a 20% chance that a significant difference is missed. Also NB writing up any kind of analysis is a field-specific exercise. Beta is directly related to study power (Power = 1 - β). Intel® FPGA Power and Thermal Calculator (PTC)estimates power supply and system thermal requirements before compiling the design, or anytime during the design phase. Beta: The probability of a type-II error - not detecting a difference when one actually exists. The Intel® Quartus® Prime Design Suite provides tools to analyze the power consumption of your FPGA design at different stages of the design process.Most medical literature uses an alpha cut-off of 5% (0.05) - indicating a 5% chance that a significant difference is actually due to chance and is not a true difference. Alpha: The probability of a type-I error - finding a difference when a difference does not exist.Treatment Effect Size: If the difference between two treatments is small, more patients will be required to detect a difference.Population Variance: The higher the variance (standard deviation), the more patients are needed to demonstrate a difference.

Baseline Incidence: If an outcome occurs infrequently, many more patients are needed in order to detect a difference.Generally speaking, statistical power is determined by the following variables: Enrolling too many patients can be unnecessarily costly or time-consuming. By enrolling too few subjects, a study may not have enough statistical power to detect a difference (type II error). 1īefore a study is conducted, investigators need to determine how many subjects should be included. This calculator uses a number of different equations to determine the minimum number of subjects that need to be enrolled in a study in order to have sufficient statistical power to detect a treatment effect.

Gpower analysis