91Ó°ÊÓ

The power of a test is the probability that testing will result in the null hypothesis being rejected for a given average value in the alternative hypothesis, and it is equivalent

to $1-\mathrm{β}$.

Increasing the $\mathrm{Î\pm }$, decreasing the $\mathrm{β}$, as well as increasing the sample size n are three parameters that will boost the power of a test.

1. Increase the value of $\mathrm{Î\pm }$
2. Sample size increase
3. Distance between increase ${\mathrm{μ}}_{\mathrm{Î\pm }}$(that is,actual value) as well as the null hypothesis value.

Power of the test =$1-\mathrm{β}$, where$\mathrm{β}$is the probability of committing type II error.The probability of making a type II error is one minus the test's power, commonly known as beta. With a larger sample size, the power of the test can be enhanced, lowering the chance of making a type II mistake.

The link between $\mathrm{β}$ and test power is $1-\mathrm{β}$