|1||T test power|
Z test power
|One Sample Z-Test|
One Sample T-Test
Two Sample Z-Test
Two Sample T-Test (Pooled variance)
Two Sample T-Test (Welch's)
|2||Chi-Squared test power||Chi-Squared Test For Goodness Of Fit|
|3||Regression power, ANOVA power||Simple Linear Regression|
Multiple Linear Regression
One way ANOVA
|4||F test power||F test for variances|
|6||Proportion power||One sample proportion test.|
Two sample proportion test.
The power calculator computes the test power based on the sample size and draw an accurate power analysis chart.
Larger sample size increases the statistical power.
The test power is the probability to reject the null assumption, H0, when it is not correct.
Power = 1- β.
Researchers usually use the power of 0.8 which means the Beta level (β), the maximum probability of type II error, failure to reject an incorrect H0, is 0.2.
The commonly used significance level (α), the maximum probability of type I error, is 0.05.
The Beta level (β) is usually four times as big as the significance level (α), since rejecting a correct null assumption consider to be more severe than failing to reject incorrect null assumption.
The calculators create the following dynamic chart:
Region of Acceptance - accept the null hypothesis if the statistic value in this area.
Region of Rejection - reject the null hypothesis if the statistic value in this area.
Grey area - The probability to accept the H0 when H0 is correct.
Significance level (α) - The probability to reject the H0 when H0 is correct.
β: the probability to accept the H0 when H1 is correct.
Test power: The probability to reject the H0 when H1 is correct.