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Many of the test statistics calculated on the other pages report a p-value. p-values are associated with type I errors. In particular, they are the probability (under the null hypothesis) that a given result would have been achieved by random chance. Therefore, a result is only considered statistically significant if its p-value is below a predetermined threshold.
While p-values are used to minimize the probability of a type I error, statistical power is related to type II errors. Power is the probability of correctly rejecting the null hypothesis. For example, statistical power answers the following question "Assuming exercise has a positive impact on mood, how likely is the experiment to come to the correct conclusion?".
The power of an experiment depends on a number of factors:
Use this calculator to compute the power of an experiment designed to determine if two data sets are significantly different from each other.
Use this calculator to compute the power of an experiment designed to determine if more than two data sets are significantly different from each other.
Note: This calculator assumes sphericity (i.e. nonsphericity correction = 1).