The precision with which the data are measured also influences statistical power. How increased sample size translates to higher power is a measure of the efficiency of the test - for example, the sample size required for a given power. Increasing sample size is often the easiest way to boost the statistical power of a test.

Other things being equal, effects are harder to detect in smaller samples. The sample size determines the amount of sampling error inherent in a test result. An unstandardized (direct) effect size is rarely sufficient to determine the power, as it does not contain information about the variability in the measurements.ДЄn example of how sample size affects power levels If constructed appropriately, a standardized effect size, along with the sample size, will completely determine the power.

It can be equivalently thought of as the probability of accepting the alternative hypothesis ( H 1 is the common standard deviation of the outcomes in the treated and control groups. However, experiment E is consequently more reliable than experiment F due to its lower probability of a typeВ I error. This reduces experiment E's sensitivity to detect significant effects. For example, if experiment E has a statistical power of 0.7, and experiment F has a statistical power of 0.95, then there is a stronger probability that experiment E had a typeВ II error than experiment F.

Illustration of the power and the significance level of a statistical test, given the null hypothesis (sampling distribution 1) and the alternative hypothesis (sampling distribution 2).ДЇor a typeВ II error probability of ОІ, the corresponding statistical power is 1В в€’ ОІ.

Statistics hypothesis test calculator